L1B_P.py 38.2 KB
Newer Older
Daniel Scheffler's avatar
Daniel Scheffler committed
1
# -*- coding: utf-8 -*-
2
3
4
5
6
7
"""
Level 1B Processor:

Detection of global/local geometric displacements.
"""

Daniel Scheffler's avatar
Daniel Scheffler committed
8

9
import collections
10
import os
11
import time
12
import warnings
13
from datetime import datetime, timedelta
14
15

import numpy as np
16
from geopandas import GeoDataFrame
17
from shapely.geometry import box
18
import pytz
19
import traceback
20
from typing import Union, TYPE_CHECKING  # noqa F401  # flake8 issue
Daniel Scheffler's avatar
Daniel Scheffler committed
21

22
from arosics import COREG, DESHIFTER
23
from geoarray import GeoArray
24
25
26
27
28
29
from py_tools_ds.geo.coord_grid import is_coord_grid_equal
from py_tools_ds.geo.coord_calc import corner_coord_to_minmax
from py_tools_ds.geo.coord_trafo import reproject_shapelyGeometry, transform_any_prj
from py_tools_ds.geo.projection import prj_equal, EPSG2WKT, WKT2EPSG
from py_tools_ds.geo.vector.topology import get_overlap_polygon

30
from ..options.config import GMS_config as CFG
31
from ..model.gms_object import GMS_object
32
33
34
35
from .L1A_P import L1A_object
from ..misc import database_tools as DB_T
from ..misc import helper_functions as HLP_F
from ..misc import path_generator as PG
36
from ..misc.logging import GMS_logger
37
from ..misc.spatial_index_mediator import SpatialIndexMediator
38
from ..misc.definition_dicts import get_GMS_sensorcode, get_outFillZeroSaturated
39

40
if TYPE_CHECKING:
Daniel Scheffler's avatar
Daniel Scheffler committed
41
42
    from shapely.geometry import Polygon  # noqa F401  # flake8 issue
    from logging import Logger  # noqa F401  # flake8 issue
43

44
__author__ = 'Daniel Scheffler'
45
46


47
class Scene_finder(object):
48
49
    """Scene_finder class to query the postgreSQL database to find a suitable reference scene for co-registration."""

50
    def __init__(self, src_boundsLonLat, src_AcqDate, src_prj, src_footprint_poly, sceneID_excluded=None,
51
                 min_overlap=20, min_cloudcov=0, max_cloudcov=20, plusminus_days=30, plusminus_years=10, logger=None):
52
        # type: (list, datetime, str, Polygon, int, int, int, int, int, int, Logger) -> None
53
54
55
56
57
58
59
60
61
62
63
64
65
        """Initialize Scene_finder.

        :param src_boundsLonLat:
        :param src_AcqDate:
        :param src_prj:
        :param src_footprint_poly:
        :param sceneID_excluded:
        :param min_overlap:         minimum overlap of reference scene in percent
        :param min_cloudcov:        minimum cloud cover of reference scene in percent
        :param max_cloudcov:        maximum cloud cover of reference scene in percent
        :param plusminus_days:      maximum time interval between target and reference scene in days
        :param plusminus_years:     maximum time interval between target and reference scene in years
        """
66
67
68
        self.boundsLonLat = src_boundsLonLat
        self.src_AcqDate = src_AcqDate
        self.src_prj = src_prj
69
        self.src_footprint_poly = src_footprint_poly
70
        self.sceneID_excluded = sceneID_excluded
71
72
73
74
75
        self.min_overlap = min_overlap
        self.min_cloudcov = min_cloudcov
        self.max_cloudcov = max_cloudcov
        self.plusminus_days = plusminus_days
        self.plusminus_years = plusminus_years
76
        self.logger = logger or GMS_logger('ReferenceSceneFinder')
77

78
        # get temporal constraints
79
        def add_years(dt, years): return dt.replace(dt.year + years) \
80
81
82
            if not (dt.month == 2 and dt.day == 29) else dt.replace(dt.year + years, 3, 1)
        self.timeStart = add_years(self.src_AcqDate, -plusminus_years)
        timeEnd = add_years(self.src_AcqDate, +plusminus_years)
83
84
        timeNow = datetime.utcnow().replace(tzinfo=pytz.UTC)
        self.timeEnd = timeEnd if timeEnd <= timeNow else timeNow
85

86
87
88
        self.possib_ref_scenes = None  # set by self.spatial_query()
        self.GDF_ref_scenes = GeoDataFrame()  # set by self.spatial_query()
        self.ref_scene = None
89

90
    def spatial_query(self, timeout=5):
91
92
93
94
        """Query the postgreSQL database to find possible reference scenes matching the specified criteria.

        :param timeout:     maximum query duration allowed (seconds)
        """
95
96
97
98
99
100
101
102
103
104
        SpIM = SpatialIndexMediator(host=CFG.spatial_index_server_host, port=CFG.spatial_index_server_port,
                                    timeout=timeout, retries=10)
        self.possib_ref_scenes = SpIM.getFullSceneDataForDataset(envelope=self.boundsLonLat,
                                                                 timeStart=self.timeStart,
                                                                 timeEnd=self.timeEnd,
                                                                 minCloudCover=self.min_cloudcov,
                                                                 maxCloudCover=self.max_cloudcov,
                                                                 datasetid=CFG.datasetid_spatial_ref,
                                                                 refDate=self.src_AcqDate,
                                                                 maxDaysDelta=self.plusminus_days)
105

106
107
108
        if self.possib_ref_scenes:
            # fill GeoDataFrame with possible ref scene parameters
            GDF = GeoDataFrame(self.possib_ref_scenes, columns=['object'])
109
110
111
112
            GDF['sceneid'] = list(GDF['object'].map(lambda scene: scene.sceneid))
            GDF['acquisitiondate'] = list(GDF['object'].map(lambda scene: scene.acquisitiondate))
            GDF['cloudcover'] = list(GDF['object'].map(lambda scene: scene.cloudcover))
            GDF['polyLonLat'] = list(GDF['object'].map(lambda scene: scene.polyLonLat))
113

114
115
            def LonLat2UTM(polyLL):
                return reproject_shapelyGeometry(polyLL, 4326, self.src_prj)
116

117
118
            GDF['polyUTM'] = list(GDF['polyLonLat'].map(LonLat2UTM))
            self.GDF_ref_scenes = GDF
119

120
121
122
    def _collect_refscene_metadata(self):
        """Collect some reference scene metadata needed for later filtering."""
        GDF = self.GDF_ref_scenes
123

124
125
126
127
128
129
130
131
132
133
134
        # get overlap parameter
        def get_OL_prms(poly): return get_overlap_polygon(poly, self.src_footprint_poly)

        GDF['overlapParams'] = list(GDF['polyLonLat'].map(get_OL_prms))
        GDF['overlap area'] = list(GDF['overlapParams'].map(lambda OL_prms: OL_prms['overlap area']))
        GDF['overlap percentage'] = list(GDF['overlapParams'].map(lambda OL_prms: OL_prms['overlap percentage']))
        GDF['overlap poly'] = list(GDF['overlapParams'].map(lambda OL_prms: OL_prms['overlap poly']))
        del GDF['overlapParams']

        # get processing level of reference scenes
        procL = GeoDataFrame(
135
            DB_T.get_info_from_postgreSQLdb(CFG.conn_database, 'scenes_proc', ['sceneid', 'proc_level'],
136
137
138
139
140
141
142
143
144
145
146
147
                                            {'sceneid': list(GDF.sceneid)}), columns=['sceneid', 'proc_level'])
        GDF = GDF.merge(procL, on='sceneid', how='left')
        GDF = GDF.where(GDF.notnull(), None)  # replace NaN values with None

        # get path of binary file
        def get_path_binary(GDF_row):
            return PG.path_generator(scene_ID=GDF_row['sceneid'], proc_level=GDF_row['proc_level']) \
                .get_path_imagedata() if GDF_row['proc_level'] else None
        GDF['path_ref'] = GDF.apply(lambda GDF_row: get_path_binary(GDF_row), axis=1)
        GDF['refDs_exists'] = list(GDF['path_ref'].map(lambda p: os.path.exists(p) if p else False))

        # check if a proper entity ID can be gathered from database
148
        eID = GeoDataFrame(DB_T.get_info_from_postgreSQLdb(CFG.conn_database, 'scenes', ['id', 'entityid'],
149
150
151
152
153
154
155
156
                                                           {'id': list(GDF.sceneid)}), columns=['sceneid', 'entityid'])
        GDF = GDF.merge(eID, on='sceneid', how='left')
        self.GDF_ref_scenes = GDF.where(GDF.notnull(), None)

    def _filter_excluded_sceneID(self):
        """Filter reference scene with the same scene ID like the target scene."""
        GDF = self.GDF_ref_scenes
        if not GDF.empty:
Daniel Scheffler's avatar
Daniel Scheffler committed
157
            self.logger.info('Same ID filter:  Excluding scene with the same ID like the target scene.')
158
            self.GDF_ref_scenes = GDF.loc[GDF['sceneid'] != self.sceneID_excluded]
159
            self.logger.info('%s scenes => %s scenes' % (len(GDF), len(self.GDF_ref_scenes)))
160

161
    def _filter_by_overlap(self):
162
        """Filter all scenes with less spatial overlap than self.min_overlap."""
163
164
        GDF = self.GDF_ref_scenes
        if not GDF.empty:
Daniel Scheffler's avatar
Daniel Scheffler committed
165
166
            self.logger.info('Overlap filter:  Excluding all scenes with less than %s percent spatial overlap.'
                             % self.min_overlap)
167
            self.GDF_ref_scenes = GDF.loc[GDF['overlap percentage'] >= self.min_overlap]
168
            self.logger.info('%s scenes => %s scenes' % (len(GDF), len(self.GDF_ref_scenes)))
169

170
    def _filter_by_proc_status(self):
171
        """Filter all scenes that have not been processed before according to proc. status (at least L1A is needed)."""
172
173
        GDF = self.GDF_ref_scenes
        if not GDF.empty:
Daniel Scheffler's avatar
Daniel Scheffler committed
174
175
            self.logger.info('Processing level filter:  Exclude all scenes that have not been processed before '
                             'according to processing status (at least L1A is needed).')
176
            self.GDF_ref_scenes = GDF[GDF['proc_level'].notnull()]
177
            self.logger.info('%s scenes => %s scenes' % (len(GDF), len(self.GDF_ref_scenes)))
178

179
    def _filter_by_dataset_existance(self):
180
        """Filter all scenes where no processed data can be found on fileserver."""
181
182
        GDF = self.GDF_ref_scenes
        if not GDF.empty:
Daniel Scheffler's avatar
Daniel Scheffler committed
183
            self.logger.info('Existance filter:  Excluding all scenes where no processed data have been found.')
184
            self.GDF_ref_scenes = GDF[GDF['refDs_exists']]
185
            self.logger.info('%s scenes => %s scenes' % (len(GDF), len(self.GDF_ref_scenes)))
186

187
    def _filter_by_entity_ID_availability(self):
188
        """Filter all scenes where no proper entity ID can be found in the database (database errors)."""
189
190
        GDF = self.GDF_ref_scenes
        if not GDF.empty:
Daniel Scheffler's avatar
Daniel Scheffler committed
191
192
            self.logger.info('DB validity filter:  Exclude all scenes where no proper entity ID can be found in the '
                             'database (database errors).')
193
            self.GDF_ref_scenes = GDF[GDF['entityid'].notnull()]
194
            self.logger.info('%s scenes => %s scenes' % (len(GDF), len(self.GDF_ref_scenes)))
195

196
    def _filter_by_projection(self):
197
        """Filter all scenes that have a different projection than the target image."""
198
        GDF = self.GDF_ref_scenes[self.GDF_ref_scenes.refDs_exists]
199
200
        if not GDF.empty:
            # compare projections of target and reference image
201
202
            GDF['prj_equal'] = \
                list(GDF['path_ref'].map(lambda path_ref: prj_equal(self.src_prj, GeoArray(path_ref).prj)))
203

Daniel Scheffler's avatar
Daniel Scheffler committed
204
205
            self.logger.info('Projection filter:  Exclude all scenes that have a different projection than the target '
                             'image.')
206
            self.GDF_ref_scenes = GDF[GDF['prj_equal']]
207
            self.logger.info('%s scenes => %s scenes' % (len(GDF), len(self.GDF_ref_scenes)))
208

209
210
    def choose_ref_scene(self):
        """Choose reference scene with minimum cloud cover and maximum overlap."""
211
212
213
        if self.possib_ref_scenes:
            # First, collect some relavant reference scene metadata
            self._collect_refscene_metadata()
214

215
216
217
218
219
220
221
            # Filter possible scenes by running all filter functions
            self._filter_excluded_sceneID()
            self._filter_by_overlap()
            self._filter_by_proc_status()
            self._filter_by_dataset_existance()
            self._filter_by_entity_ID_availability()
            self._filter_by_projection()
222

223
224
225
226
227
        # Choose the reference scene out of the filtered DataFrame
        if not self.GDF_ref_scenes.empty:
            GDF = self.GDF_ref_scenes
            GDF = GDF[GDF['cloudcover'] == GDF['cloudcover'].min()]
            GDF = GDF[GDF['overlap percentage'] == GDF['overlap percentage'].max()]
228

229
230
231
232
233
            if not GDF.empty:
                GDF_res = GDF.iloc[0]
                return ref_Scene(GDF_res)
        else:
            return None
234

235

236
237
class ref_Scene:
    def __init__(self, GDF_record):
238
239
240
        self.scene_ID = int(GDF_record['sceneid'])
        self.entity_ID = GDF_record['entityid']
        self.AcqDate = GDF_record['acquisitiondate']
241
242
        self.cloudcover = GDF_record['cloudcover']
        self.polyLonLat = GDF_record['polyLonLat']
243
        self.polyUTM = GDF_record['polyUTM']
244
        self.proc_level = GDF_record['proc_level']
245
        self.filePath = GDF_record['path_ref']
246
247
248
249
250


class L1B_object(L1A_object):
    def __init__(self, L1A_obj=None):

251
        super(L1B_object, self).__init__()
252
253
254

        # set defaults
        self._spatRef_available = None
255
256
        self.spatRef_scene = None  # set by self.get_spatial_reference_scene()
        self.deshift_results = collections.OrderedDict()
257
258
259
260
261
262

        if L1A_obj:
            # populate attributes
            [setattr(self, key, value) for key, value in L1A_obj.__dict__.items()]

        self.proc_level = 'L1B'
263
        self.proc_status = 'initialized'
264
265
266

    @property
    def spatRef_available(self):
267
        if self._spatRef_available is not None:
268
269
270
271
272
273
274
275
276
277
            return self._spatRef_available
        else:
            self.get_spatial_reference_scene()
            return self._spatRef_available

    @spatRef_available.setter
    def spatRef_available(self, spatRef_available):
        self._spatRef_available = spatRef_available

    def get_spatial_reference_scene(self):
278
        boundsLonLat = corner_coord_to_minmax(self.trueDataCornerLonLat)
279
        footprint_poly = HLP_F.CornerLonLat_to_shapelyPoly(self.trueDataCornerLonLat)
280
        RSF = Scene_finder(boundsLonLat, self.acq_datetime, self.MetaObj.projection,
281
282
283
284
285
                           footprint_poly, self.scene_ID,
                           min_overlap=CFG.spatial_ref_min_overlap,
                           min_cloudcov=CFG.spatial_ref_min_cloudcov,
                           max_cloudcov=CFG.spatial_ref_max_cloudcov,
                           plusminus_days=CFG.spatial_ref_plusminus_days,
286
287
                           plusminus_years=CFG.spatial_ref_plusminus_years,
                           logger=self.logger)
288
289
290
291
292
293
294

        # run spatial query
        self.logger.info('Querying database in order to find a suitable reference scene for co-registration.')
        RSF.spatial_query(timeout=5)
        if RSF.possib_ref_scenes:
            self.logger.info('Query result: %s reference scenes with matching metadata.' % len(RSF.possib_ref_scenes))

295
296
297
298
299
300
301
302
303
304
305
            # try to get a spatial reference scene by applying some filter criteria
            self.spatRef_scene = RSF.choose_ref_scene()  # type: Union[ref_Scene, None]
            if self.spatRef_scene:
                self.spatRef_available = True
                self.logger.info('Found a suitable reference image for coregistration: scene ID %s (entity ID %s).'
                                 % (self.spatRef_scene.scene_ID, self.spatRef_scene.entity_ID))
            else:
                self.spatRef_available = False
                self.logger.warning('No scene fulfills all requirements to serve as spatial reference for scene %s '
                                    '(entity ID %s). Coregistration impossible.' % (self.scene_ID, self.entity_ID))

306
        else:
307
            self.logger.warning('Spatial query returned no matches. Coregistration impossible.')
308
            self.spatRef_available = False
309
310

    def _get_reference_image_params_pgSQL(self):
311
        # TODO implement earlier version of this function as a backup for SpatialIndexMediator
312
313
        """postgreSQL query: get IDs of overlapping scenes and select most suitable scene_ID
            (with respect to DGM, cloud cover"""
314
315
        warnings.warn('_get_reference_image_params_pgSQL is deprecated an will not work anymore.', DeprecationWarning)

316
317
        # vorab-check anhand wolkenmaske, welche region von im2shift überhaupt für shift-corr tauglich ist
        # -> diese region als argument in postgresql abfrage
318
        # scene_ID            = 14536400 # LE71510322000093SGS00 im2shift
319

320
        # set query conditions
321
322
        min_overlap = 20  # %
        max_cloudcov = 20  # %
323
        plusminus_days = 30
324
325
        AcqDate = self.im2shift_objDict['acquisition_date']
        date_minmax = [AcqDate - timedelta(days=plusminus_days), AcqDate + timedelta(days=plusminus_days)]
326
        dataset_cond = 'datasetid=%s' % CFG.datasetid_spatial_ref
327
328
329
330
331
        cloudcov_cond = 'cloudcover < %s' % max_cloudcov
        # FIXME cloudcover noch nicht für alle scenes im proc_level METADATA verfügbar
        dayrange_cond = "(EXTRACT(MONTH FROM scenes.acquisitiondate), EXTRACT(DAY FROM scenes.acquisitiondate)) " \
                        "BETWEEN (%s, %s) AND (%s, %s)" \
                        % (date_minmax[0].month, date_minmax[0].day, date_minmax[1].month, date_minmax[1].day)
332
333
        # TODO weitere Kriterien einbauen!

334
335
        def query_scenes(condlist):
            return DB_T.get_overlapping_scenes_from_postgreSQLdb(
336
                CFG.conn_database,
337
338
339
340
341
                table='scenes',
                tgt_corners_lonlat=self.trueDataCornerLonLat,
                conditions=condlist,
                add_cmds='ORDER BY scenes.cloudcover ASC',
                timeout=30000)
342
343
        conds_descImportance = [dataset_cond, cloudcov_cond, dayrange_cond]

344
        self.logger.info('Querying database in order to find a suitable reference scene for co-registration.')
345

346
        count, filt_overlap_scenes = 0, []
347
        while not filt_overlap_scenes:
348
349
350
351
            if count == 0:
                # search within already processed scenes
                # das ist nur Ergebnis aus scenes_proc
                # -> dort liegt nur eine referenz, wenn die szene schon bei CFG.job-Beginn in Datensatzliste drin war
352
                res = DB_T.get_overlapping_scenes_from_postgreSQLdb(
353
                    CFG.conn_database,
354
                    tgt_corners_lonlat=self.trueDataCornerLonLat,
355
                    conditions=['datasetid=%s' % CFG.datasetid_spatial_ref],
356
357
                    add_cmds='ORDER BY scenes.cloudcover ASC',
                    timeout=25000)
358
                filt_overlap_scenes = self._sceneIDList_to_filt_overlap_scenes([i[0] for i in res[:50]], 20.)
359

360
            else:
361
362
363
                # search within complete scenes table using less filter criteria with each run
                # TODO: Daniels Index einbauen, sonst  bei wachsender Tabellengröße evtl. irgendwann zu langsam
                res = query_scenes(conds_descImportance)
364
                filt_overlap_scenes = self._sceneIDList_to_filt_overlap_scenes([i[0] for i in res[:50]], min_overlap)
365

366
                if len(conds_descImportance) > 1:  # FIXME anderer Referenzsensor?
367
368
369
370
                    del conds_descImportance[-1]
                else:  # reduce min_overlap to 10 percent if there are overlapping scenes
                    if res:
                        min_overlap = 10
371
372
                        filt_overlap_scenes = \
                            self._sceneIDList_to_filt_overlap_scenes([i[0] for i in res[:50]], min_overlap)
373
374

                    # raise warnings if no match found
375
                    if not filt_overlap_scenes:
376
377
                        if not res:
                            warnings.warn('No reference scene found for %s (scene ID %s). Coregistration of this scene '
378
                                          'failed.' % (self.baseN, self.scene_ID))
379
380
381
                        else:
                            warnings.warn('Reference scenes for %s (scene ID %s) have been found but none has more '
                                          'than %s percent overlap. Coregistration of this scene failed.'
382
                                          % (self.baseN, self.scene_ID, min_overlap))
383
                        break
384
            count += 1
385
386
387
388

        if filt_overlap_scenes:
            ref_available = False
            for count, sc in enumerate(filt_overlap_scenes):
389
                if count == 2:  # FIXME Abbuch schon bei 3. Szene?
390
                    warnings.warn('No reference scene for %s (scene ID %s) available. '
391
                                  'Coregistration of this scene failed.' % (self.baseN, self.scene_ID))
392
393
394
                    break

                # start download of scene data not available and start L1A processing
395
                def dl_cmd(scene_ID): print('%s %s %s' % (
396
397
                    CFG.java_commands['keyword'].strip(),  # FIXME CFG.java_commands is deprecated
                    CFG.java_commands["value_download"].strip(), scene_ID))
398

399
                path = PG.path_generator(scene_ID=sc['scene_ID']).get_path_imagedata()
Daniel Scheffler's avatar
GEOP:    
Daniel Scheffler committed
400

401
402
403
404
405
406
407
408
                if not os.path.exists(path):
                    # add scene 2 download to scenes_jobs.missing_scenes

                    # print JAVA download command
                    t_dl_start = time.time()
                    dl_cmd(sc['scene_ID'])

                    # check if scene is downloading
409
410
                    download_start_timeout = 5  # seconds
                    # set timout for external processing
411
                    # -> DEPRECATED BECAUSE CREATION OF EXTERNAL CFG WITHIN CFG IS NOT ALLOWED
412
                    processing_timeout = 5  # seconds # FIXME increase timeout if processing is really started
413
414
415
                    proc_level = None
                    while True:
                        proc_level_chk = DB_T.get_info_from_postgreSQLdb(
416
                            CFG.conn_database, 'scenes', ['proc_level'], {'id': sc['scene_ID']})[0][0]
417
                        if proc_level != proc_level_chk:
418
                            print('Reference scene %s, current processing level: %s' % (sc['scene_ID'], proc_level_chk))
419
                        proc_level = proc_level_chk
420
421
                        if proc_level_chk in ['SCHEDULED', 'METADATA'] and \
                           time.time() - t_dl_start >= download_start_timeout:
422
                            warnings.warn('Download of reference scene %s (entity ID %s) timed out. '
423
                                          'Coregistration of this scene failed.' % (self.baseN, self.scene_ID))
424
425
                            break
                        if proc_level_chk == 'L1A':
426
427
428
429
                            ref_available = True
                            break
                        elif proc_level_chk == 'DOWNLOADED' and time.time() - t_dl_start >= processing_timeout:
                            # proc_level='DOWNLOADED' but scene has not been processed
Daniel Scheffler's avatar
GEOP:    
Daniel Scheffler committed
430
431
432
                            warnings.warn('L1A processing of reference scene %s (entity ID %s) timed out. '
                                          'Coregistration of this scene failed.' % (self.baseN, self.scene_ID))
                            break
433
                            # DB_T.set_info_in_postgreSQLdb(CFG.conn_database,'scenes',
434
                            #                             {'proc_level':'METADATA'},{'id':sc['scene_ID']})
Daniel Scheffler's avatar
GEOP:    
Daniel Scheffler committed
435

436
437
438
439
440
441
442
                        time.sleep(5)
                else:
                    ref_available = True

                if not ref_available:
                    continue
                else:
443
444
                    self.path_imref = path
                    self.imref_scene_ID = sc['scene_ID']
445
                    self.imref_footprint_poly = sc['scene poly']
446
447
448
449
                    self.overlap_poly = sc['overlap poly']
                    self.overlap_percentage = sc['overlap percentage']
                    self.overlap_area = sc['overlap area']

450
                    query_res = DB_T.get_info_from_postgreSQLdb(CFG.conn_database, 'scenes', ['entityid'],
451
452
453
                                                                {'id': self.imref_scene_ID}, records2fetch=1)
                    assert query_res != [], 'No entity-ID found for scene number %s' % self.imref_scene_ID
                    self.imref_entity_ID = query_res[0][0]  # [('LC81510322013152LGN00',)]
454
                    break
455
        self.logger.close()
456

457
    def _sceneIDList_to_filt_overlap_scenes(self, sceneIDList, min_overlap):
458
459
460
        """find reference scenes that cover at least 20% of the scene with the given ID
        ONLY FIRST 50 scenes are considered"""

461
462
463
        # t0 = time.time()
        dict_sceneID_poly = [{'scene_ID': ID, 'scene poly': HLP_F.scene_ID_to_shapelyPolygon(ID)}
                             for ID in sceneIDList]  # always returns LonLot polygons
464
465

        # get overlap polygons and their parameters. result: [{overlap poly, overlap percentage, overlap area}]
466
467
        for dic in dict_sceneID_poly:  # input: dicts {scene_ID, ref_poly}
            dict_overlap_poly_params = get_overlap_polygon(dic['scene poly'], self.arr.footprint_poly)
468
            dic.update(dict_overlap_poly_params)  # adds {overlap poly, overlap percentage, overlap area}
469
        # print('polygon creation time', time.time()-t0)
470
471
472
473
474
475
476
477

        # filter those scene_IDs out where overlap percentage is below 20%
        if min_overlap:
            filt_overlap_scenes = [scene for scene in dict_sceneID_poly if scene['overlap percentage'] >= min_overlap]
        else:
            filt_overlap_scenes = dict_sceneID_poly

        return filt_overlap_scenes
478

479
    def get_opt_bands4matching(self, target_cwlPos_nm=550):
480
481
482
483
        """Automatically determines the optimal bands used für fourier shift theorem matching

        :param target_cwlPos_nm:   the desired wavelength used for matching
        """
484
485
        # get GMS_object for reference scene
        path_gmsFile = PG.path_generator(scene_ID=self.spatRef_scene.scene_ID).get_path_gmsfile()
486
        ref_obj = GMS_object.from_disk((path_gmsFile, ['cube', None]))
487

488
        # get spectral characteristics
Daniel Scheffler's avatar
Bugfix.    
Daniel Scheffler committed
489
490
491
        ref_cwl = [float(ref_obj.MetaObj.CWL[bN]) for bN in ref_obj.MetaObj.LayerBandsAssignment]
        shift_cwl = [float(self.MetaObj.CWL[bN]) for bN in self.MetaObj.LayerBandsAssignment]
        shift_fwhm = [float(self.MetaObj.FWHM[bN]) for bN in self.MetaObj.LayerBandsAssignment]
492
493

        # exclude cirrus/oxygen band of Landsat-8/Sentinel-2
494
        shift_bbl, ref_bbl = [False] * len(shift_cwl), [False] * len(ref_cwl)  # bad band lists
495
        for GMS_obj, s_r, bbl in zip([self, ref_obj], ['shift', 'ref'], [shift_bbl, ref_bbl]):
496
            GMS_obj.GMS_identifier.logger = None  # set a dummy value in order to avoid Exception
497
498
499
500
501
            sensorcode = get_GMS_sensorcode(GMS_obj.GMS_identifier)
            if sensorcode in ['LDCM', 'S2A', 'S2B'] and '9' in GMS_obj.LayerBandsAssignment:
                bbl[GMS_obj.LayerBandsAssignment.index('9')] = True
            if sensorcode in ['S2A', 'S2B'] and '10' in GMS_obj.LayerBandsAssignment:
                bbl[GMS_obj.LayerBandsAssignment.index('10')] = True
502

503
        # cwl_overlap = (max(min(shift_cwl),min(ref_cwl)),  min(max(shift_cwl),max(ref_cwl))) # -> (min wvl, max wvl)
504
        # find matching band of reference image for each band of image to be shifted
505
506
507
508
        match_dic = collections.OrderedDict()
        for idx, cwl, fwhm in zip(range(len(shift_cwl)), shift_cwl, shift_fwhm):
            if shift_bbl[idx]:
                continue  # skip cwl if it is declared as bad band above
509
510
511

            def is_inside(r_cwl, s_cwl, s_fwhm): return s_cwl - s_fwhm / 2 < r_cwl < s_cwl + s_fwhm / 2

512
513
            matching_r_cwls = [r_cwl for i, r_cwl in enumerate(ref_cwl) if
                               is_inside(r_cwl, cwl, fwhm) and not ref_bbl[i]]
514
515
            if matching_r_cwls:
                match_dic[cwl] = matching_r_cwls[0] if len(matching_r_cwls) else \
516
                    matching_r_cwls[np.abs(np.array(matching_r_cwls) - cwl).argmin()]
517
518
519
520
521

        # set bands4 match based on the above results
        poss_cwls = [cwl for cwl in shift_cwl if cwl in match_dic]
        if poss_cwls:
            if not target_cwlPos_nm:
522
523
524
525
526
527
528
                shift_band4match = shift_cwl.index(poss_cwls[0]) + 1  # first possible shift band
                ref_band4match = ref_cwl.index(match_dic[poss_cwls[0]]) + 1  # matching reference band
            else:  # target_cwlPos is given
                closestWvl_to_target = poss_cwls[np.abs(np.array(poss_cwls) - target_cwlPos_nm).argmin()]
                shift_band4match = shift_cwl.index(closestWvl_to_target) + 1  # the shift band closest to target
                ref_band4match = ref_cwl.index(match_dic[closestWvl_to_target]) + 1  # matching ref
        else:  # all reference bands are outside of shift-cwl +- fwhm/2
529
530
            self.logger.warning('Optimal bands for matching could not be automatically determined. '
                                'Choosing first band of each image.')
531
532
            shift_band4match = 1
            ref_band4match = 1
533

534
        self.logger.info(
535
            'Target band for matching:    %s (%snm)' % (shift_band4match, shift_cwl[shift_band4match - 1]))
536
537
        self.logger.info(
            'Reference band for matching: %s (%snm)' % (ref_band4match, ref_cwl[ref_band4match - 1]))
538
539
540

        return ref_band4match, shift_band4match

541
    def compute_global_shifts(self):
542
543
544
545
546
        spatIdxSrv_status = os.environ['GMS_SPAT_IDX_SRV_STATUS'] if 'GMS_SPAT_IDX_SRV_STATUS' in os.environ else True

        if spatIdxSrv_status == 'unavailable':
            self.logger.warning('Coregistration skipped due to unavailable Spatial Index Mediator Server!"')

547
        elif CFG.skip_coreg:
548
            self.logger.warning('Coregistration skipped according to user configuration.')
549

550
        elif self.coreg_needed and self.spatRef_available:
551
552
553
            self.coreg_info.update({'reference scene ID': self.spatRef_scene.scene_ID})
            self.coreg_info.update({'reference entity ID': self.spatRef_scene.entity_ID})

554
555
            geoArr_ref = GeoArray(self.spatRef_scene.filePath)
            geoArr_shift = GeoArray(self.arr)
556
            r_b4match, s_b4match = self.get_opt_bands4matching(target_cwlPos_nm=CFG.coreg_band_wavelength_for_matching)
557
558
559
560
561
            coreg_kwargs = dict(
                r_b4match=r_b4match,
                s_b4match=s_b4match,
                align_grids=True,  # FIXME not needed here
                match_gsd=True,  # FIXME not needed here
562
                max_shift=CFG.coreg_max_shift_allowed,
Daniel Scheffler's avatar
Fix.    
Daniel Scheffler committed
563
                ws=CFG.coreg_window_size,
564
                data_corners_ref=[[x, y] for x, y in self.spatRef_scene.polyUTM.convex_hull.exterior.coords],
565
                data_corners_tgt=[transform_any_prj(EPSG2WKT(4326), self.MetaObj.projection, x, y)
566
567
568
569
570
571
572
                                  for x, y in HLP_F.reorder_CornerLonLat(self.trueDataCornerLonLat)],
                nodata=(get_outFillZeroSaturated(geoArr_ref.dtype)[0],
                        get_outFillZeroSaturated(geoArr_shift.dtype)[0]),
                ignore_errors=True,
                v=False,
                q=True
            )
573

574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
            # initialize COREG object
            try:
                COREG_obj = COREG(geoArr_ref, geoArr_shift, **coreg_kwargs)
            except Exception as e:
                COREG_obj = None
                self.logger.error('\nAn error occurred during coregistration. BE AWARE THAT THE SCENE %s '
                                  '(ENTITY ID %s) HAS NOT BEEN COREGISTERED! Error message was: \n%s\n'
                                  % (self.scene_ID, self.entity_ID, repr(e)))
                self.logger.error(traceback.format_exc())
                # TODO include that in the job summary

            # calculate_spatial_shifts
            if COREG_obj:
                COREG_obj.calculate_spatial_shifts()

                self.coreg_info.update(
                    COREG_obj.coreg_info)  # no clipping to trueCornerLonLat until here -> only shift correction
                self.coreg_info.update({'shift_reliability': COREG_obj.shift_reliability})

                if COREG_obj.success:
                    self.coreg_info['success'] = True
                    self.logger.info("Calculated map shifts (X,Y): %s / %s"
                                     % (COREG_obj.x_shift_map,
                                        COREG_obj.y_shift_map))  # FIXME direkt in calculate_spatial_shifts loggen
                    self.logger.info("Reliability of calculated shift: %.1f percent" % COREG_obj.shift_reliability)
599

600
601
602
603
                else:
                    # TODO add database entry with error hint
                    [self.logger.error('ERROR during coregistration of scene %s (entity ID %s):\n%s'
                                       % (self.scene_ID, self.entity_ID, err)) for err in COREG_obj.tracked_errors]
604

605
        else:
606
            if self.coreg_needed:
607
608
                self.logger.warning('Coregistration skipped because no suitable reference scene is available or '
                                    'spatial query failed.')
609
610
            else:
                self.logger.info('Coregistration of scene %s (entity ID %s) skipped because target dataset ID equals '
611
612
                                 'reference dataset ID.' % (self.scene_ID, self.entity_ID))

613
614
    def correct_spatial_shifts(self, cliptoextent=True, clipextent=None, clipextent_prj=None, v=False):
        # type: (bool, list, any, bool) -> None
615
        """Corrects the spatial shifts calculated by self.compute_global_shifts().
616
617
618
619
620
621
622
623
624

        :param cliptoextent:    whether to clip the output to the given extent
        :param clipextent:      list of XY-coordinate tuples giving the target extent (if not given and cliptoextent is
                                True, the 'trueDataCornerLonLat' attribute of the GMS object is used
        :param clipextent_prj:  WKT projection string or EPSG code of the projection for the coordinates in clipextent
        :param v:
        :return:
        """

625
626
        # cliptoextent is automatically True if an extent is given
        cliptoextent = cliptoextent if not clipextent else True
627

628
629
        if cliptoextent or self.resamp_needed or (self.coreg_needed and self.coreg_info['success']):

630
            # get target bounds # TODO implement boxObj call instead here
631
            if not clipextent:
632
633
                trueDataCornerUTM = [transform_any_prj(EPSG2WKT(4326), self.MetaObj.projection, x, y)
                                     for x, y in self.trueDataCornerLonLat]
634
                xmin, xmax, ymin, ymax = corner_coord_to_minmax(trueDataCornerUTM)
635
                mapBounds = box(xmin, ymin, xmax, ymax).bounds
636
637
638
639
640
641
642
643
            else:
                assert clipextent_prj, \
                    "'clipextent_prj' must be given together with 'clipextent'. Received only 'clipextent'."
                clipextent_UTM = clipextent if prj_equal(self.MetaObj.projection, clipextent_prj) else \
                    [transform_any_prj(clipextent_prj, self.MetaObj.projection, x, y) for x, y in clipextent]
                xmin, xmax, ymin, ymax = corner_coord_to_minmax(clipextent_UTM)
                mapBounds = box(xmin, ymin, xmax, ymax).bounds

644
            # correct shifts and clip to extent
645
646
            # ensure self.masks exists (does not exist in case of inmem_serialization mode because
            # then self.fill_from_disk() is skipped)
647
648
649
            if not hasattr(self, 'masks') or self.masks is None:
                self.build_combined_masks_array()  # creates self.masks and self.masks_meta

650
651
652
            # exclude self.mask_nodata, self.mask_clouds from warping
            del self.mask_nodata, self.mask_clouds

653
654
655
            attributes2deshift = [attrname for attrname in
                                  ['arr', 'masks', 'dem', 'ac_errors', 'mask_clouds_confidence']
                                  if getattr(self, '_%s' % attrname) is not None]
656
            for attrname in attributes2deshift:
657
                geoArr = getattr(self, attrname)
658
659

                # do some logging
660
661
                if self.coreg_needed:
                    if self.coreg_info['success']:
662
663
                        self.logger.info("Correcting spatial shifts for attribute '%s'..." % attrname)
                    elif cliptoextent and is_coord_grid_equal(
664
                         geoArr.gt, CFG.spatial_ref_gridx, CFG.spatial_ref_gridy):
665
                        self.logger.info("Attribute '%s' has only been clipped to it's extent because no valid "
666
667
                                         "shifts have been detected and the pixel grid equals the target grid."
                                         % attrname)
668
669
                    elif self.resamp_needed:
                        self.logger.info("Resampling attribute '%s' to target grid..." % attrname)
670
671
672
673
                elif self.resamp_needed:
                    self.logger.info("Resampling attribute '%s' to target grid..." % attrname)

                # correct shifts
674
                DS = DESHIFTER(geoArr, self.coreg_info,
675
                               target_xyGrid=[CFG.spatial_ref_gridx, CFG.spatial_ref_gridy],
676
677
678
                               cliptoextent=cliptoextent,
                               clipextent=mapBounds,
                               align_grids=True,
679
                               resamp_alg='nearest' if attrname == 'masks' else CFG.spatial_resamp_alg,
680
                               CPUs=None if CFG.allow_subMultiprocessing else 1,
681
682
683
                               progress=True if v else False,
                               q=True,
                               v=v)
684
685
686
                DS.correct_shifts()

                # update coreg_info
687
688
                if attrname == 'arr':
                    self.coreg_info['is shifted'] = DS.is_shifted
689
                    self.coreg_info['is resampled'] = DS.is_resampled
690

691
                # update geoinformations and array shape related attributes
692
693
                self.logger.info("Updating geoinformations of '%s' attribute..." % attrname)
                if attrname == 'arr':
694
695
                    self.MetaObj.map_info = DS.updated_map_info
                    self.MetaObj.projection = EPSG2WKT(WKT2EPSG(DS.updated_projection))
696
                    self.shape_fullArr = DS.arr_shifted.shape
697
                    self.MetaObj.rows, self.MetaObj.cols = DS.arr_shifted.shape[:2]
698
                else:
699
700
                    self.masks_meta['map info'] = DS.updated_map_info
                    self.masks_meta['coordinate system string'] = EPSG2WKT(WKT2EPSG(DS.updated_projection))
701
702
                    self.masks_meta['lines'], self.masks_meta['samples'] = DS.arr_shifted.shape[:2]

703
704
                    # NOTE: mask_nodata and mask_clouds are updated later by L2A_map mapper function (module pipeline)

705
                # update the GeoArray instance without loosing its inherent metadata (nodata, ...)
706
707
708
                geoArr.arr, geoArr.gt, geoArr.prj = \
                    DS.GeoArray_shifted.arr, DS.GeoArray_shifted.gt, DS.GeoArray_shifted.prj
                # setattr(self,attrname, DS.GeoArray_shifted) # NOTE: don't set array earlier because setter will also
709
                #                                             # update arr.gt/.prj/.nodata from MetaObj
710

711
            self.resamp_needed = False
712
            self.coreg_needed = False
713

714
715
            # recreate self.masks_nodata and self.mask_clouds from self.masks
            self.mask_nodata = self.mask_nodata
716
717
            self.mask_clouds = self.mask_clouds
            # FIXME move functionality of self.masks only to output writer and remove self.masks completely