CoReg.py 78 KB
Newer Older
1
2
3
4
5
6
7
8
9
# -*- coding: utf-8 -*-
__author__='Daniel Scheffler'

import os
import re
import shutil
import subprocess
import time
import warnings
10
from copy import copy
11
12

# custom
13
14
15
16
try:
    import gdal
except ImportError:
    from osgeo import gdal
17
import numpy as np
18
19
20
try:
    import pyfftw
except ImportError:
21
    pyfftw = None
22
from shapely.geometry import Point, Polygon
23
from skimage.exposure import rescale_intensity
24
25

# internal modules
26
from .DeShifter import DESHIFTER, _dict_rspAlg_rsp_Int
27
28
29
30
from .          import geometry  as GEO
from .          import io        as IO
from .          import plotting  as PLT

31
from py_tools_ds.ptds.io.raster.GeoArray   import GeoArray, BadDataMask
32
from py_tools_ds.ptds.geo.coord_calc       import corner_coord_to_minmax, get_corner_coordinates
33
from py_tools_ds.ptds.geo.vector.topology  import get_overlap_polygon, get_smallest_boxImYX_that_contains_boxMapYX
34
from py_tools_ds.ptds.geo.projection       import prj_equal, get_proj4info
35
36
from py_tools_ds.ptds.geo.vector.geometry  import boxObj, round_shapelyPoly_coords
from py_tools_ds.ptds.geo.coord_grid       import move_shapelyPoly_to_image_grid
37
from py_tools_ds.ptds.geo.coord_trafo      import pixelToMapYX, reproject_shapelyGeometry, mapXY2imXY
38
39
40
from py_tools_ds.ptds.geo.raster.reproject import warp_ndarray
from py_tools_ds.ptds.geo.map_info         import geotransform2mapinfo
from py_tools_ds.ptds.numeric.vector       import find_nearest
41
from py_tools_ds.ptds.similarity.raster    import calc_ssim
42
43
44
45




46
class GeoArray_CoReg(GeoArray):
47
    def __init__(self, CoReg_params, imID):
48
49
        # type: (dict, str) -> None

50
        assert imID in ['ref', 'shift']
Daniel Scheffler's avatar
CoReg:    
Daniel Scheffler committed
51

52
53
54
55
56
57
        # run GeoArray init
        path_or_geoArr = CoReg_params['im_ref'] if imID == 'ref' else CoReg_params['im_tgt']
        nodata         = CoReg_params['nodata'][0 if imID == 'ref' else 1]
        progress       = CoReg_params['progress']
        q              = CoReg_params['q'] if not CoReg_params['v'] else False

58
        super(GeoArray_CoReg, self).__init__(path_or_geoArr, nodata=nodata, progress=progress, q=q)
59
60

        self.imID   = imID
61
        self.imName = 'reference image' if imID == 'ref' else 'image to be shifted'
62
63
64
        self.v      = CoReg_params['v']

        assert isinstance(self, GeoArray), \
65
66
67
68
            'Something went wrong with the creation of GeoArray instance for the %s. The created ' \
            'instance does not seem to belong to the GeoArray class. If you are working in Jupyter Notebook, reset the ' \
            'kernel and try again.' %self.imName

69
        # set title to be used in plots
70
        self.title = os.path.basename(self.filePath) if self.filePath else self.imName
71
72
73
74
75
76
77

        # validate params
        assert self.prj, 'The %s has no projection.' % self.imName
        assert not re.search('LOCAL_CS', self.prj), 'The %s is not georeferenced.' % self.imName
        assert self.gt, 'The %s has no map information.' % self.imName

        # set band4match
78
        self.band4match = (CoReg_params['r_b4match'] if imID == 'ref' else CoReg_params['s_b4match'])-1
79
80
81
        assert self.bands >= self.band4match+1 >= 1, "The %s has %s %s. So its band number to match must be %s%s. " \
            "Got %s." % (self.imName, self.bands, 'bands' if self.bands > 1 else 'band', 'between 1 and '
            if self.bands > 1 else '', self.bands, self.band4match)
82

83
84
85
86
87
        # set footprint_poly
        given_footprint_poly = CoReg_params['footprint_poly_%s' % ('ref' if imID == 'ref' else 'tgt')]
        given_corner_coord   = CoReg_params['data_corners_%s'   % ('ref' if imID == 'ref' else 'tgt')]

        if given_footprint_poly:
88
            self.footprint_poly = given_footprint_poly
89
        elif given_corner_coord is not None:
90
            self.footprint_poly = Polygon(given_corner_coord)
91
92
        elif not CoReg_params['calc_corners']:
            # use the image extent
93
            self.footprint_poly = Polygon(get_corner_coordinates(gt=self.gt, cols=self.cols,rows=self.rows))
94
        else:
95
96
97
            # footprint_poly is calculated automatically by GeoArray
            if not CoReg_params['q']:
                print('Calculating actual data corner coordinates for %s...' % self.imName)
98
            self.calc_mask_nodata(fromBand=self.band4match)  # this avoids that all bands have to be read
99

100
        self.poly = self.footprint_poly  # returns a shapely geometry
101

102
        if not self.q:
103
            print('Bounding box of calculated footprint for %s:\n\t%s' % (self.imName, self.poly.bounds))
104

105
106
107
        # add bad data mask
        given_mask = CoReg_params['mask_baddata_%s' % ('ref' if imID == 'ref' else 'tgt')]
        if given_mask:
108
            self.mask_baddata = BadDataMask(given_mask)
109

110
111
112


class COREG(object):
113
114
    """See help(COREG) for documentation!"""

115
116
    def __init__(self, im_ref, im_tgt, path_out=None, fmt_out='ENVI', out_crea_options=None, r_b4match=1, s_b4match=1,
                 wp=(None,None), ws=(512, 512), max_iter=5, max_shift=5, align_grids=False, match_gsd=False,
117
118
                 out_gsd=None, target_xyGrid=None, resamp_alg_deshift='cubic', resamp_alg_calc='cubic',
                 footprint_poly_ref=None, footprint_poly_tgt=None, data_corners_ref=None, data_corners_tgt=None,
119
                 nodata=(None,None), calc_corners=True, binary_ws=True, mask_baddata_ref=None, mask_baddata_tgt=None,
Daniel Scheffler's avatar
Daniel Scheffler committed
120
                 CPUs=None, force_quadratic_win=True, progress=True, v=False, path_verbose_out=None, q=False,
121
                 ignore_errors=False):
122
123
124
125

        """Detects and corrects global X/Y shifts between a target and refernce image. Geometric shifts are calculated
        at a specific (adjustable) image position. Correction performs a global shifting in X- or Y direction.

126
127
128
129
        :param im_ref(str, GeoArray):   source path (any GDAL compatible image format is supported) or GeoArray instance
                                        of reference image
        :param im_tgt(str, GeoArray):   source path (any GDAL compatible image format is supported) or GeoArray instance
                                        of image to be shifted
130
        :param path_out(str):           target path of the coregistered image
131
132
133
                                            - if None (default), the method correct_shifts() does not write to disk
                                            - if 'auto': /dir/of/im1/<im1>__shifted_to__<im0>.bsq
        :param fmt_out(str):            raster file format for output file. ignored if path_out is None. can be any GDAL
134
135
                                        compatible raster file format (e.g. 'ENVI', 'GeoTIFF'; default: ENVI). Refer to
                                        http://www.gdal.org/formats_list.html to get a full list of supported formats.
136
137
        :param out_crea_options(list):  GDAL creation options for the output image,
                                        e.g. ["QUALITY=80", "REVERSIBLE=YES", "WRITE_METADATA=YES"]
138
139
140
141
142
143
144
145
146
147
148
        :param r_b4match(int):          band of reference image to be used for matching (starts with 1; default: 1)
        :param s_b4match(int):          band of shift image to be used for matching (starts with 1; default: 1)
        :param wp(tuple):               custom matching window position as map values in the same projection like the
                                        reference image (default: central position of image overlap)
        :param ws(tuple):               custom matching window size [pixels] (default: (512,512))
        :param max_iter(int):           maximum number of iterations for matching (default: 5)
        :param max_shift(int):          maximum shift distance in reference image pixel units (default: 5 px)
        :param align_grids(bool):       align the coordinate grids of the image to be and the reference image (default: 0)
        :param match_gsd(bool):         match the output pixel size to pixel size of the reference image (default: 0)
        :param out_gsd(tuple):          xgsd ygsd: set the output pixel size in map units
                                        (default: original pixel size of the image to be shifted)
149
150
        :param target_xyGrid(list):     a list with a target x-grid and a target y-grid like [[15,45], [15,45]]
                                        This overrides 'out_gsd', 'align_grids' and 'match_gsd'.
151
152
153
154
155
156
157
158
159
        :param resamp_alg_deshift(str)  the resampling algorithm to be used for shift correction (if neccessary)
                                        valid algorithms: nearest, bilinear, cubic, cubic_spline, lanczos, average, mode,
                                                          max, min, med, q1, q3
                                        default: cubic
        :param resamp_alg_calc(str)     the resampling algorithm to be used for all warping processes during calculation
                                        of spatial shifts
                                        (valid algorithms: nearest, bilinear, cubic, cubic_spline, lanczos, average, mode,
                                                       max, min, med, q1, q3)
                                        default: cubic (highly recommended)
160
161
162
163
164
165
166
167
168
169
        :param footprint_poly_ref(str): footprint polygon of the reference image (WKT string or shapely.geometry.Polygon),
                                        e.g. 'POLYGON ((299999 6000000, 299999 5890200, 409799 5890200, 409799 6000000,
                                                        299999 6000000))'
        :param footprint_poly_tgt(str): footprint polygon of the image to be shifted (WKT string or shapely.geometry.Polygon)
                                        e.g. 'POLYGON ((299999 6000000, 299999 5890200, 409799 5890200, 409799 6000000,
                                                        299999 6000000))'
        :param data_corners_ref(list):  map coordinates of data corners within reference image.
                                        ignored if footprint_poly_ref is given.
        :param data_corners_tgt(list):  map coordinates of data corners within image to be shifted.
                                        ignored if footprint_poly_tgt is given.
170
171
172
173
174
        :param nodata(tuple):           no data values for reference image and image to be shifted
        :param calc_corners(bool):      calculate true positions of the dataset corners in order to get a useful
                                        matching window position within the actual image overlap
                                        (default: 1; deactivated if '-cor0' and '-cor1' are given
        :param binary_ws(bool):         use binary X/Y dimensions for the matching window (default: 1)
175
176
177
178
179
180
181
182
183
184
185
186
        :param mask_baddata_ref(str, GeoArray): path to a 2D boolean mask file (or an instance of GeoArray) for the
                                                reference image where all bad data pixels (e.g. clouds) are marked with
                                                True and the remaining pixels with False. Must have the same geographic
                                                extent and projection like 'im_ref'. The mask is used to check if the
                                                chosen matching window position is valid in the sense of useful data.
                                                Otherwise this window position is rejected.
        :param mask_baddata_tgt(str, GeoArray): path to a 2D boolean mask file (or an instance of GeoArray) for the
                                                image to be shifted where all bad data pixels (e.g. clouds) are marked
                                                with True and the remaining pixels with False. Must have the same
                                                geographic extent and projection like 'im_ref'. The mask is used to
                                                check if the chosen matching window position is valid in the sense of
                                                useful data. Otherwise this window position is rejected.
Daniel Scheffler's avatar
Daniel Scheffler committed
187
188
        :param CPUs(int):               number of CPUs to use during pixel grid equalization
                                        (default: None, which means 'all CPUs available')
189
        :param force_quadratic_win(bool):   force a quadratic matching window (default: 1)
190
        :param progress(bool):          show progress bars (default: True)
191
        :param v(bool):                 verbose mode (default: False)
192
193
        :param path_verbose_out(str):   an optional output directory for intermediate results
                                        (if not given, no intermediate results are written to disk)
194
195
        :param q(bool):                 quiet mode (default: False)
        :param ignore_errors(bool):     Useful for batch processing. (default: False)
196
197
198
199
200
201
                                        In case of error COREG.success == False and COREG.x_shift_px/COREG.y_shift_px
                                        is None
        """

        self.params              = dict([x for x in locals().items() if x[0] != "self"])

202
        # assertions
203
        assert gdal.GetDriverByName(fmt_out), "'%s' is not a supported GDAL driver." % fmt_out
204
205
        if match_gsd and out_gsd: warnings.warn("'-out_gsd' is ignored because '-match_gsd' is set.\n")
        if out_gsd:  assert isinstance(out_gsd, list) and len(out_gsd) == 2, 'out_gsd must be a list with two values.'
206
207
208
209
        if data_corners_ref and not isinstance(data_corners_ref[0], list): # group if not [[x,y],[x,y]..] but [x,y,x,y,]
            data_corners_ref = [data_corners_ref[i:i + 2] for i in range(0, len(data_corners_ref), 2)]
        if data_corners_tgt and not isinstance(data_corners_tgt[0], list): # group if not [[x,y],[x,y]..]
            data_corners_tgt = [data_corners_tgt[i:i + 2] for i in range(0, len(data_corners_tgt), 2)]
210
211
        if nodata: assert isinstance(nodata, tuple) and len(nodata) == 2, "'nodata' must be a tuple with two values." \
                                                                          "Got %s with length %s." %(type(nodata),len(nodata))
212
        for rspAlg in [resamp_alg_deshift, resamp_alg_calc]:
213
            assert rspAlg in _dict_rspAlg_rsp_Int.keys(), "'%s' is not a supported resampling algorithm." % rspAlg
214
        if resamp_alg_calc=='average' and (v or not q):
215
            warnings.warn("The resampling algorithm 'average' causes sinus-shaped patterns in fft images that will "
216
217
                          "affect the precision of the calculated spatial shifts! It is highly recommended to "
                          "choose another resampling algorithm.")
218
219

        self.path_out            = path_out            # updated by self.set_outpathes
220
        self.fmt_out             = fmt_out
221
        self.out_creaOpt         = out_crea_options
222
223
224
225
226
227
228
        self.win_pos_XY          = wp                  # updated by self.get_opt_winpos_winsize()
        self.win_size_XY         = ws                  # updated by self.get_opt_winpos_winsize()
        self.max_iter            = max_iter
        self.max_shift           = max_shift
        self.align_grids         = align_grids
        self.match_gsd           = match_gsd
        self.out_gsd             = out_gsd
229
        self.target_xyGrid       = target_xyGrid
230
231
        self.rspAlg_DS           = resamp_alg_deshift
        self.rspAlg_calc         = resamp_alg_calc
232
        self.calc_corners        = calc_corners
Daniel Scheffler's avatar
Daniel Scheffler committed
233
        self.CPUs                = CPUs
234
235
236
237
        self.bin_ws              = binary_ws
        self.force_quadratic_win = force_quadratic_win
        self.v                   = v
        self.path_verbose_out    = path_verbose_out
238
239
240
        self.q                   = q if not v else False # overridden by v
        self.progress            = progress if not q else False  # overridden by q

241
242
243
244
        self.ignErr              = ignore_errors
        self.max_win_sz_changes  = 3                   # TODO: änderung der window size, falls nach max_iter kein valider match gefunden
        self.ref                 = None                # set by self.get_image_params
        self.shift               = None                # set by self.get_image_params
245
246
247
248
        self.matchBox            = None                # set by self.get_clip_window_properties()  => boxObj
        self.otherBox            = None                # set by self.get_clip_window_properties()  => boxObj
        self.matchWin            = None                # set by self._get_image_windows_to_match() => GeoArray
        self.otherWin            = None                # set by self._get_image_windows_to_match() => GeoArray
249
        self.imfft_gsd           = None                # set by self.get_clip_window_properties()
250
        self.fftw_works          = None                # set by self._calc_shifted_cross_power_spectrum()
251
        self.fftw_win_size_YX    = None                # set by calc_shifted_cross_power_spectrum()
252
253
254
255
256

        self.x_shift_px          = None                # always in shift image units (image coords) # set by calculate_spatial_shifts()
        self.y_shift_px          = None                # always in shift image units (image coords) # set by calculate_spatial_shifts()
        self.x_shift_map         = None                # set by self.get_updated_map_info()
        self.y_shift_map         = None                # set by self.get_updated_map_info()
257
258
        self.vec_length_map      = None
        self.vec_angle_deg       = None
259
        self.updated_map_info    = None                # set by self.get_updated_map_info()
260
261
262
        self.ssim_orig           = None                # set by self._validate_ssim_improvement()
        self.ssim_deshifted      = None                # set by self._validate_ssim_improvement()
        self._ssim_improved      = None                # private attribute to be filled by self.ssim_improved
263
        self.shift_reliability   = None                # set by self.calculate_spatial_shifts()
264
265

        self.tracked_errors      = []                  # expanded each time an error occurs
266
        self.success             = None                # default
267
        self.deshift_results     = None                # set by self.correct_shifts()
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290

        gdal.AllRegister()
        self._get_image_params()
        self._set_outpathes(im_ref, im_tgt)
        self.grid2use                 = 'ref' if self.shift.xgsd <= self.ref.xgsd else 'shift'
        if self.v: print('resolutions: ', self.ref.xgsd, self.shift.xgsd)

        overlap_tmp                   = get_overlap_polygon(self.ref.poly, self.shift.poly, self.v)
        self.overlap_poly             = overlap_tmp['overlap poly'] # has to be in reference projection
        assert self.overlap_poly, 'The input images have no spatial overlap.'
        self.overlap_percentage       = overlap_tmp['overlap percentage']
        self.overlap_area             = overlap_tmp['overlap area']

        if self.v and self.path_verbose_out:
            IO.write_shp(os.path.join(self.path_verbose_out, 'poly_imref.shp'),    self.ref.poly,     self.ref.prj)
            IO.write_shp(os.path.join(self.path_verbose_out, 'poly_im2shift.shp'), self.shift.poly,   self.shift.prj)
            IO.write_shp(os.path.join(self.path_verbose_out, 'overlap_poly.shp'),  self.overlap_poly, self.ref.prj)

        ### FIXME: transform_mapPt1_to_mapPt2(im2shift_center_map, ds_imref.GetProjection(), ds_im2shift.GetProjection()) # später basteln für den fall, dass projektionen nicht gleich sind

        # get_clip_window_properties
        self._get_opt_winpos_winsize()
        if not self.q: print('Matching window position (X,Y): %s/%s' % (self.win_pos_XY[0], self.win_pos_XY[1]))
291
        self._get_clip_window_properties() # sets self.matchBox, self.otherBox and much more
292

293
        if self.v and self.path_verbose_out and self.matchBox.mapPoly and self.success is not False:
294
            IO.write_shp(os.path.join(self.path_verbose_out, 'poly_matchWin.shp'),
295
                         self.matchBox.mapPoly, self.matchBox.prj)
296

297
        self.success     = False if self.success is False or not self.matchBox.boxMapYX else None
298
        self._coreg_info = None # private attribute to be filled by self.coreg_info property
299
300
301


    def _set_outpathes(self, im_ref, im_tgt):
302
303
304
305
        assert isinstance(im_ref, (GeoArray, str)) and isinstance(im_tgt, (GeoArray, str)),\
            'COREG._set_outpathes() expects two file pathes (string) or two instances of the ' \
            'GeoArray class. Received %s and %s.' %(type(im_ref), type(im_tgt))

306
307
308
309
310
311
        get_baseN = lambda path: os.path.splitext(os.path.basename(path))[0]

        # get input pathes
        path_im_ref = im_ref.filePath if isinstance(im_ref, GeoArray) else im_ref
        path_im_tgt = im_tgt.filePath if isinstance(im_tgt, GeoArray) else im_tgt

312
313
314
315
316
        if self.path_out: # this also applies to self.path_out='auto'

            if self.path_out == 'auto':
                dir_out, fName_out = os.path.dirname(path_im_tgt), ''
            else:
317
                dir_out, fName_out = os.path.split(self.path_out)
318
319
320
321
322
323
324
325
326
327
328
329
330
331

            if dir_out and fName_out:
                # a valid output path is given => do nothing
                pass

            else:
                # automatically create an output directory and filename if not given
                if not dir_out:
                    if not path_im_ref:
                        dir_out = os.path.abspath(os.path.curdir)
                    else:
                        dir_out = os.path.dirname(path_im_ref)

                if not fName_out:
332
333
334
335
336
                    ext         = 'bsq' if self.fmt_out=='ENVI' else \
                                    gdal.GetDriverByName(self.fmt_out).GetMetadataItem(gdal.DMD_EXTENSION)
                    fName_out   = fName_out if not fName_out in ['.',''] else '%s__shifted_to__%s' \
                                    %(get_baseN(path_im_tgt), get_baseN(path_im_ref))
                    fName_out   = fName_out+'.%s'%ext if ext else fName_out
337

338
                self.path_out   = os.path.abspath(os.path.join(dir_out,fName_out))
339
340
341
342

                assert ' ' not in self.path_out, \
                    "The path of the output image contains whitespaces. This is not supported by GDAL."
        else:
343
            # this only happens if COREG is not instanced from within Python and self.path_out is explicitly set to None
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
            # => DESHIFTER will return an array
            pass

        if self.v:
            if self.path_verbose_out:
                dir_out, dirname_out = os.path.split(self.path_verbose_out)

                if not dir_out:
                    if self.path_out:
                        self.path_verbose_out = os.path.dirname(self.path_out)
                    else:
                        self.path_verbose_out = os.path.abspath(os.path.join(os.path.curdir,
                            'CoReg_verboseOut__%s__shifted_to__%s' % (get_baseN(path_im_tgt), get_baseN(path_im_ref))))
                elif dirname_out and not dir_out:
                    self.path_verbose_out = os.path.abspath(os.path.join(os.path.curdir, dirname_out))

                assert ' ' not in self.path_verbose_out, \
                    "'path_verbose_out' contains whitespaces. This is not supported by GDAL."

        else:
            self.path_verbose_out = None

        if self.path_verbose_out and not os.path.isdir(self.path_verbose_out): os.makedirs(self.path_verbose_out)


    def _get_image_params(self):
370
371
        self.ref   = GeoArray_CoReg(self.params,'ref')
        self.shift = GeoArray_CoReg(self.params,'shift')
372
        assert prj_equal(self.ref.prj, self.shift.prj), \
373
374
            'Input projections are not equal. Different projections are currently not supported. Got %s / %s.'\
            %(get_proj4info(proj=self.ref.prj), get_proj4info(proj=self.shift.prj))
375
376


377
378
379
380
381
    def equalize_pixGrids(self):
        """
        Equalize image grids and projections of reference and target image (align target to reference).
        """
        if not (prj_equal(self.ref.prj, self.shift.prj) and self.ref.xygrid_specs==self.shift.xygrid_specs):
Daniel Scheffler's avatar
Daniel Scheffler committed
382
383
            if not self.q: print("Equalizing pixel grids and projections of reference and target image...")

384
            self.shift.arr = self.shift[:,:,self.shift.band4match]
Daniel Scheffler's avatar
Daniel Scheffler committed
385
            self.shift.reproject_to_new_grid(prototype=self.ref, CPUs=self.CPUs)
386
387


388
389
390
391
392
393
394
395
396
    def show_image_footprints(self):
        """This method is intended to be called from Jupyter Notebook and shows a web map containing the calculated
        footprints of the input images as well as the corresponding overlap area."""
        # TODO different colors for polygons
        assert self.overlap_poly, 'Please calculate the overlap polygon first.'

        try:
            import folium, geojson
        except ImportError:
397
398
            folium, geojson = None, None
        if not folium or not geojson:
399
400
401
            raise ImportError("This method requires the libraries 'folium' and 'geojson'. They can be installed with "
                              "the shell command 'pip install folium geojson'.")

402
403
404
405
        refPoly      = reproject_shapelyGeometry(self.ref  .poly      , self.ref  .epsg, 4326)
        shiftPoly    = reproject_shapelyGeometry(self.shift.poly      , self.shift.epsg, 4326)
        overlapPoly  = reproject_shapelyGeometry(self.overlap_poly    , self.shift.epsg, 4326)
        matchBoxPoly = reproject_shapelyGeometry(self.matchBox.mapPoly, self.shift.epsg, 4326)
406
407

        m = folium.Map(location=tuple(np.array(overlapPoly.centroid.coords.xy).flatten())[::-1])
408
        for poly in [refPoly, shiftPoly, overlapPoly, matchBoxPoly]:
409
410
411
412
413
            gjs = geojson.Feature(geometry=poly, properties={})
            folium.GeoJson(gjs).add_to(m)
        return m


414
415
    def show_matchWin(self, figsize=(15,15), interactive=True, deshifted=False):
        """Show the image content within the matching window.
416

417
418
419
420
421
        :param figsize:      <tuple> figure size
        :param interactive:  <bool> whether to return an interactive figure based on 'holoviews' library
        :param deshifted:    <bool> whether to put the image content AFTER shift correction into the figure
        :return:
        """
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
        if interactive:
            # use Holoviews
            try:
                import holoviews as hv
            except ImportError:
                hv =None
            if not hv:
                raise ImportError(
                    "This method requires the library 'holoviews'. It can be installed for Anaconda with "
                    "the shell command 'conda install -c ioam holoviews bokeh'.")
            warnings.filterwarnings('ignore')
            hv.notebook_extension('matplotlib')
            hv.Store.add_style_opts(hv.Image, ['vmin','vmax'])

            #hv.Store.option_setters.options().Image = hv.Options('style', cmap='gnuplot2')
            #hv.Store.add_style_opts(hv.Image, ['cmap'])
            #renderer = hv.Store.renderers['matplotlib'].instance(fig='svg', holomap='gif')
            #RasterPlot = renderer.plotting_class(hv.Image)
            #RasterPlot.cmap = 'gray'
441
442
            otherWin_corr       = self._get_deshifted_otherWin()
            xmin,xmax,ymin,ymax = self.matchBox.boundsMap
443
444
445
446


            get_vmin     = lambda arr: np.percentile(arr, 2)
            get_vmax     = lambda arr: np.percentile(arr, 98)
447
448
449
            rescale      = lambda arr: rescale_intensity(arr, in_range=(get_vmin(arr), get_vmax(arr)))
            get_arr      = lambda geoArr: rescale(np.ma.masked_equal(geoArr[:], geoArr.nodata))
            get_hv_image = lambda geoArr: hv.Image(get_arr(geoArr), bounds=(xmin,ymin,xmax,ymax))(
450
                style={'cmap':'gray',
451
                       'vmin':get_vmin(geoArr[:]), 'vmax':get_vmax(geoArr[:]), # does not work
452
                       'interpolation':'none'},
453
                plot={'fig_inches':figsize, 'show_grid':True})
454
455
                #plot={'fig_size':100, 'show_grid':True})

456
457
458
            imgs_orig = {1 : get_hv_image(self.matchWin), 2 : get_hv_image(self.otherWin)}
            imgs_corr = {1 : get_hv_image(self.matchWin), 2 : get_hv_image(otherWin_corr)}
            #layout = get_hv_image(self.matchWin) + get_hv_image(self.otherWin)
459

460
461
            imgs = {1 : get_hv_image(self.matchWin) + get_hv_image(self.matchWin),
                    2 : get_hv_image(self.otherWin) + get_hv_image(otherWin_corr)
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
                        }

            # Construct a HoloMap by evaluating the function over all the keys
            hmap_orig = hv.HoloMap(imgs_orig, kdims=['image'])
            hmap_corr = hv.HoloMap(imgs_corr, kdims=['image'])

            hmap      = hv.HoloMap(imgs, kdims=['image']).collate().cols(1) # displaying this results in a too small figure
            #hmap = hv.HoloMap(imgs_corr, kdims=['image']) +  hv.HoloMap(imgs_corr, kdims=['image'])

            ## Construct a HoloMap by defining the sampling on the Dimension
            #dmap = hv.DynamicMap(image_slice, kdims=[hv.Dimension('z_axis', values=keys)])
            warnings.filterwarnings('default')
            #return hmap

            return hmap_orig if not deshifted else hmap_corr

478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
        else:
            # TODO add titles
            self.matchWin.show(figsize=figsize)
            if deshifted:
                self._get_deshifted_otherWin().show(figsize=figsize)
            else:
                self.otherWin.show(figsize=figsize)


    def show_cross_power_spectrum(self, interactive=False):
        """
        Shows a 3D surface of the cross power spectrum resulting from phase correlating the reference and target
        image within the matching window.

        :param interactive:  whether to return an interactice 3D surface plot based on 'plotly' library
        :return:
        """

        if interactive:
            # create plotly 3D surface

            #import plotly.plotly as py # online mode -> every plot is uploaded into online plotly account
            from plotly.offline import iplot, init_notebook_mode
            import plotly.graph_objs as go

            init_notebook_mode(connected=True)

            z_data = self._calc_shifted_cross_power_spectrum()
            data   = [go.Surface(z=z_data)]
            layout = go.Layout(
                title='cross power spectrum',
                autosize=False,
                width=1000,
                height=1000,
                margin=dict(l=65, r=50, b=65, t=90))
            fig    = go.Figure(data=data, layout=layout)

            return iplot(fig, filename='SCPS')

        else:
            # use matplotlib
            scps = self._calc_shifted_cross_power_spectrum()
            PLT.subplot_3dsurface(scps.astype(np.float32))

522

523
    def _get_opt_winpos_winsize(self):
524
        # type: (tuple,tuple) -> None
525
526
527
528
        """
        Calculates optimal window position and size in reference image units according to DGM, cloud_mask and
        trueCornerLonLat.
        """
529
530
531
532
533
534
535
536
537
538
539
540
        # dummy algorithm: get center position of overlap instead of searching ideal window position in whole overlap
        # TODO automatischer Algorithmus zur Bestimmung der optimalen Window Position

        wp = tuple(self.win_pos_XY)
        assert type(self.win_pos_XY) in [tuple,list,np.ndarray],\
            'The window position must be a tuple of two elements. Got %s with %s elements.' %(type(wp),len(wp))
        wp = tuple(wp)

        if None in wp:
            overlap_center_pos_x, overlap_center_pos_y = self.overlap_poly.centroid.coords.xy
            wp = (wp[0] if wp[0] else overlap_center_pos_x[0]), (wp[1] if wp[1] else overlap_center_pos_y[0])

541
        # validate window position
542
543
544
545
546
547
        if not self.overlap_poly.contains(Point(wp)):
            self.success=False
            self.tracked_errors.append(ValueError('The provided window position %s/%s is outside of the overlap ' \
                                                  'area of the two input images. Check the coordinates.' %wp))
            if not self.ignErr:
                raise self.tracked_errors[-1]
548
549
550
551
552
553

        # check if window position is within bad data area if a respective mask has been provided
        for im in [self.ref, self.shift]:
            if im.mask_baddata is not None:
                imX, imY = mapXY2imXY(wp, im.mask_baddata.gt)

554
                if im.mask_baddata[int(imY), int(imX)] is True:
555
556
557
558
559
560
561
                    self.tracked_errors.append(
                        RuntimeError('According to the provided bad data mask for the %s the chosen window position '
                            '%s / %s is within a bad data area. Using this window position for coregistration '
                            'is not reasonable. Please provide a better window position!' %(im.imName, wp[0], wp[1])))
                    self.success = False
                    if not self.ignErr:
                        raise self.tracked_errors[-1]
562

563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
        self.win_pos_XY  = wp
        self.win_size_XY = (int(self.win_size_XY[0]), int(self.win_size_XY[1])) if self.win_size_XY else (512,512)


    def _get_clip_window_properties(self):
        """Calculate all properties of the matching window and the other window. These windows are used to read the
        corresponding image positions in the reference and the target image.
        hint: Even if X- and Y-dimension of the target window is equal, the output window can be NOT quadratic!
        """
        # FIXME image sizes like 10000*256 are still possible

        wpX,wpY             = self.win_pos_XY
        wsX,wsY             = self.win_size_XY
        ref_wsX, ref_wsY    = (wsX*self.ref.xgsd  , wsY*self.ref.ygsd)   # image units -> map units
        shift_wsX,shift_wsY = (wsX*self.shift.xgsd, wsY*self.shift.ygsd) # image units -> map units
        ref_box_kwargs      = {'wp':(wpX,wpY),'ws':(ref_wsX,ref_wsY)    ,'gt':self.ref.gt  }
        shift_box_kwargs    = {'wp':(wpX,wpY),'ws':(shift_wsX,shift_wsY),'gt':self.shift.gt}
580
581
        matchBox            = boxObj(**ref_box_kwargs)   if self.grid2use=='ref' else boxObj(**shift_box_kwargs)
        otherBox            = boxObj(**shift_box_kwargs) if self.grid2use=='ref' else boxObj(**ref_box_kwargs)
582
583
584
        overlapWin          = boxObj(mapPoly=self.overlap_poly,gt=self.ref.gt)

        # clip matching window to overlap area
585
586
587
588
589
590
591
592
593
594
595
596
597
        matchBox.mapPoly = matchBox.mapPoly.intersection(overlapWin.mapPoly)

        #check if matchBox extent touches no data area of the image -> if yes: shrink it
        overlapPoly_within_matchWin = matchBox.mapPoly.intersection(self.overlap_poly)
        if overlapPoly_within_matchWin.area < matchBox.mapPoly.area:
            wsX_start, wsY_start = 1 if wsX>=wsY else wsX/wsY, 1 if wsY>=wsX else wsY/wsX
            box = boxObj(**dict(wp=(wpX,wpY),ws=(wsX_start, wsY_start), gt=matchBox.gt))
            while True:
                box.buffer_imXY(1,1)
                if not box.mapPoly.within(overlapPoly_within_matchWin):
                    box.buffer_imXY(-1, -1)
                    matchBox = box
                    break
598
599
600

        # move matching window to imref grid or im2shift grid
        mW_rows, mW_cols = (self.ref.rows, self.ref.cols) if self.grid2use == 'ref' else (self.shift.rows, self.shift.cols)
601
        matchBox.mapPoly = move_shapelyPoly_to_image_grid(matchBox.mapPoly, matchBox.gt, mW_rows, mW_cols, 'NW')
602

603
604
        # check, ob durch Verschiebung auf Grid die matchBox außerhalb von overlap_poly geschoben wurde
        if not matchBox.mapPoly.within(overlapWin.mapPoly):
605
            # matchPoly weiter verkleinern # 1 px buffer reicht, weil window nur auf das Grid verschoben wurde
606
607
            xLarger,yLarger = matchBox.is_larger_DimXY(overlapWin.boundsIm)
            matchBox.buffer_imXY(-1 if xLarger else 0, -1 if yLarger else 0)
608
609

        # matching_win direkt auf grid2use (Rundungsfehler bei Koordinatentrafo beseitigen)
610
        matchBox.imPoly = round_shapelyPoly_coords(matchBox.imPoly, precision=0, out_dtype=int)
611
612

        # Check, ob match Fenster größer als anderes Fenster
613
        if not (matchBox.mapPoly.within(otherBox.mapPoly) or matchBox.mapPoly==otherBox.mapPoly):
614
            # dann für anderes Fenster kleinstes Fenster finden, das match-Fenster umgibt
615
            otherBox.boxImYX = get_smallest_boxImYX_that_contains_boxMapYX(matchBox.boxMapYX,otherBox.gt)
616
617

        # evtl. kann es sein, dass bei Shift-Fenster-Vergrößerung das shift-Fenster zu groß für den overlap wird
618
        while not otherBox.mapPoly.within(overlapWin.mapPoly):
619
            # -> match Fenster verkleinern und neues anderes Fenster berechnen
620
621
622
623
            xLarger, yLarger = otherBox.is_larger_DimXY(overlapWin.boundsIm)
            matchBox.buffer_imXY(-1 if xLarger else 0, -1 if yLarger else 0)
            previous_area    = otherBox.mapPoly.area
            otherBox.boxImYX = get_smallest_boxImYX_that_contains_boxMapYX(matchBox.boxMapYX,otherBox.gt)
624

625
            if previous_area == otherBox.mapPoly.area:
626
627
628
629
630
631
632
633
634
635
636
637
                self.tracked_errors.append(
                    RuntimeError('Matching window in target image is larger than overlap area but further shrinking '
                                 'the matching window is not possible. Check if the footprints of the input data have '
                                 'been computed correctly. '))
                if not self.ignErr:
                    raise self.tracked_errors[-1]
                break # break out of while loop in order to avoid that code gets stuck here

        if self.tracked_errors:
            self.success = False
        else:
            # check results
638
639
            assert matchBox.mapPoly.within(otherBox.mapPoly)
            assert otherBox.mapPoly.within(overlapWin.mapPoly)
640
641

            self.imfft_gsd              = self.ref.xgsd       if self.grid2use =='ref' else self.shift.xgsd
642
643
            self.ref.win,self.shift.win = (matchBox,otherBox) if self.grid2use =='ref' else (otherBox,matchBox)
            self.matchBox,self.otherBox = matchBox, otherBox
644
645
            self.ref.  win.size_YX      = tuple([int(i) for i in self.ref.  win.imDimsYX])
            self.shift.win.size_YX      = tuple([int(i) for i in self.shift.win.imDimsYX])
646
            match_win_size_XY           = tuple(reversed([int(i) for i in matchBox.imDimsYX]))
647
648
649
            if not self.q and match_win_size_XY != self.win_size_XY:
                print('Target window size %s not possible due to too small overlap area or window position too close '
                      'to an image edge. New matching window size: %s.' %(self.win_size_XY,match_win_size_XY))
650
651
            #IO.write_shp('/misc/hy5/scheffler/Temp/matchMapPoly.shp', matchBox.mapPoly,matchBox.prj)
            #IO.write_shp('/misc/hy5/scheffler/Temp/otherMapPoly.shp', otherBox.mapPoly,otherBox.prj)
652
653
654
655
656
657
658


    def _get_image_windows_to_match(self):
        """Reads the matching window and the other window using subset read, and resamples the other window to the
        resolution and the pixel grid of the matching window. The result consists of two images with the same
        dimensions and exactly the same corner coordinates."""

659
660
        match_fullGeoArr = self.ref   if self.grid2use=='ref' else self.shift
        other_fullGeoArr = self.shift if self.grid2use=='ref' else self.ref
661
662

        # matchWin per subset-read einlesen -> self.matchWin.data
663
        rS, rE, cS, cE = GEO.get_GeoArrayPosition_from_boxImYX(self.matchBox.boxImYX)
664
        assert np.array_equal(np.abs(np.array([rS,rE,cS,cE])), np.array([rS,rE,cS,cE])), \
665
            'Got negative values in gdalReadInputs for %s.' %match_fullGeoArr.imName
666
667
668
669
670
        self.matchWin = GeoArray(match_fullGeoArr[rS:rE,cS:cE, match_fullGeoArr.band4match],
                                 geotransform = GEO.get_subset_GeoTransform(match_fullGeoArr.gt, self.matchBox.boxImYX),
                                 projection   = copy(match_fullGeoArr.prj),
                                 nodata       = copy(match_fullGeoArr.nodata))
        self.matchWin.imID = match_fullGeoArr.imID
671
672

        # otherWin per subset-read einlesen
673
        rS, rE, cS, cE = GEO.get_GeoArrayPosition_from_boxImYX(self.otherBox.boxImYX)
674
        assert np.array_equal(np.abs(np.array([rS,rE,cS,cE])), np.array([rS,rE,cS,cE])), \
675
            'Got negative values in gdalReadInputs for %s.' %other_fullGeoArr.imName
676
677
678
679
680
        self.otherWin = GeoArray(other_fullGeoArr[rS:rE, cS:cE, other_fullGeoArr.band4match],
                                 geotransform = GEO.get_subset_GeoTransform(other_fullGeoArr.gt, self.otherBox.boxImYX),
                                 projection   = copy(other_fullGeoArr.prj),
                                 nodata       = copy(other_fullGeoArr.nodata))
        self.otherWin.imID = other_fullGeoArr.imID
681
682
683

        #self.matchWin.deepcopy_array()
        #self.otherWin.deepcopy_array()
684
685
686

        if self.v:
            print('Original matching windows:')
687
688
            ref_data, shift_data =  (self.matchWin[:], self.otherWin[:]) if self.grid2use=='ref' else \
                                    (self.otherWin[:], self.matchWin[:])
689
690
            PLT.subplot_imshow([ref_data, shift_data],[self.ref.title,self.shift.title], grid=True)

691
        # resample otherWin.arr to the resolution of matchWin AND make sure the pixel edges are identical
692
693
        # (in order to make each image show the same window with the same coordinates)
        # TODO replace cubic resampling by PSF resampling - average resampling leads to sinus like distortions in the fft image that make a precise coregistration impossible. Thats why there is currently no way around cubic resampling.
694
        tgt_xmin,tgt_xmax,tgt_ymin,tgt_ymax = self.matchBox.boundsMap
695
696
697
698
699
700
701
702
703
704
705
706

        # equalize pixel grids and projection of matchWin and otherWin (ONLY if grids are really different)
        if not(self.matchWin.xygrid_specs==self.otherWin.xygrid_specs and
            prj_equal(self.matchWin.prj, self.otherWin.prj)):
            self.otherWin.arr, self.otherWin.gt = warp_ndarray(self.otherWin.arr,
                                                               self.otherWin.gt,
                                                               self.otherWin.prj,
                                                               self.matchWin.prj,
                                                               out_gsd    = (self.imfft_gsd, self.imfft_gsd),
                                                               out_bounds = ([tgt_xmin, tgt_ymin, tgt_xmax, tgt_ymax]),
                                                               rspAlg     = _dict_rspAlg_rsp_Int[self.rspAlg_calc],
                                                               in_nodata  = self.otherWin.nodata,
Daniel Scheffler's avatar
Daniel Scheffler committed
707
                                                               CPUs       = self.CPUs,
708
                                                               progress   = False) [:2]
709
710

        if self.matchWin.shape != self.otherWin.shape:
711
712
            self.tracked_errors.append(
                RuntimeError('Bad output of get_image_windows_to_match. Reference image shape is %s whereas shift '
713
                             'image shape is %s.' % (self.matchWin.shape, self.otherWin.shape)))
714
            raise self.tracked_errors[-1]
715
716
        rows, cols = [i if i % 2 == 0 else i - 1 for i in self.matchWin.shape]
        self.matchWin.arr, self.otherWin.arr = self.matchWin.arr[:rows, :cols], self.otherWin.arr[:rows, :cols]
717

718
        assert self.matchWin.arr is not None and self.otherWin.arr is not None, 'Creation of matching windows failed.'
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745


    @staticmethod
    def _shrink_winsize_to_binarySize(win_shape_YX, target_size=None):
        # type: (tuple, tuple, int , int) -> tuple
        """Shrinks a given window size to the closest binary window size (a power of 2) -
        separately for X- and Y-dimension.

        :param win_shape_YX:    <tuple> source window shape as pixel units (rows,colums)
        :param target_size:     <tuple> source window shape as pixel units (rows,colums)
        """

        binarySizes   = [2**i for i in range(3,14)] # [8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]
        possibSizes_X = [i for i in binarySizes if i <= win_shape_YX[1]]
        possibSizes_Y = [i for i in binarySizes if i <= win_shape_YX[0]]
        if possibSizes_X and possibSizes_Y:
            tgt_size_X,tgt_size_Y = target_size if target_size else (max(possibSizes_X),max(possibSizes_Y))
            closest_to_target_X = int(min(possibSizes_X, key=lambda x:abs(x-tgt_size_X)))
            closest_to_target_Y = int(min(possibSizes_Y, key=lambda y:abs(y-tgt_size_Y)))
            return closest_to_target_Y,closest_to_target_X
        else:
            return None


    def _calc_shifted_cross_power_spectrum(self, im0=None, im1=None, precision=np.complex64):
        """Calculates shifted cross power spectrum for quantifying x/y-shifts.

746
747
748
749
        :param im0:         reference image
        :param im1:         subject image to shift
        :param precision:   to be quantified as a datatype
        :return:            2D-numpy-array of the shifted cross power spectrum
750
751
        """

752
753
754
        im0 = im0 if im0 is not None else self.matchWin[:] if self.matchWin.imID=='ref'   else self.otherWin[:]
        im1 = im1 if im1 is not None else self.otherWin[:] if self.otherWin.imID=='shift' else self.matchWin[:]

755
756
757
758
759
        assert im0.shape == im1.shape, 'The reference and the target image must have the same dimensions.'
        if im0.shape[0]%2!=0: warnings.warn('Odd row count in one of the match images!')
        if im1.shape[1]%2!=0: warnings.warn('Odd column count in one of the match images!')

        wsYX = self._shrink_winsize_to_binarySize(im0.shape) if self.bin_ws              else im0.shape
760
        wsYX = ((min(wsYX),) * 2                             if self.force_quadratic_win else wsYX) if wsYX else None
761
762
763
764
765
766
767

        if wsYX:
            time0 = time.time()
            if self.v: print('final window size: %s/%s (X/Y)' % (wsYX[1], wsYX[0]))
            center_YX = np.array(im0.shape)/2
            xmin,xmax,ymin,ymax = int(center_YX[1]-wsYX[1]/2), int(center_YX[1]+wsYX[1]/2),\
                                  int(center_YX[0]-wsYX[0]/2), int(center_YX[0]+wsYX[0]/2)
768

769
770
771
772
773
774
775
            in_arr0  = im0[ymin:ymax,xmin:xmax].astype(precision)
            in_arr1  = im1[ymin:ymax,xmin:xmax].astype(precision)

            if self.v:
                PLT.subplot_imshow([in_arr0.astype(np.float32), in_arr1.astype(np.float32)],
                               ['FFTin '+self.ref.title,'FFTin '+self.shift.title], grid=True)

776
            if pyfftw and self.fftw_works is not False: # if module is installed and working
777
778
                fft_arr0 = pyfftw.FFTW(in_arr0,np.empty_like(in_arr0), axes=(0,1))()
                fft_arr1 = pyfftw.FFTW(in_arr1,np.empty_like(in_arr1), axes=(0,1))()
779
780
781
782
783
784
785
786
787

                # catch empty output arrays (for some reason this happens sometimes..) -> use numpy fft
                if self.fftw_works is None and (np.std(fft_arr0)==0 or np.std(fft_arr1)==0):
                    self.fftw_works = False
                    # recreate input arrays and use numpy fft as fallback
                    in_arr0 = im0[ymin:ymax, xmin:xmax].astype(precision)
                    in_arr1 = im1[ymin:ymax, xmin:xmax].astype(precision)
                    fft_arr0 = np.fft.fft2(in_arr0)
                    fft_arr1 = np.fft.fft2(in_arr1)
788
789
                else:
                    self.fftw_works = True
790
791
792
            else:
                fft_arr0 = np.fft.fft2(in_arr0)
                fft_arr1 = np.fft.fft2(in_arr1)
793

794
795
796
            #GeoArray(fft_arr0.astype(np.float32)).show(figsize=(15,15))
            #GeoArray(fft_arr1.astype(np.float32)).show(figsize=(15,15))

797
798
799
800
801
            if self.v: print('forward FFTW: %.2fs' %(time.time() -time0))

            eps = np.abs(fft_arr1).max() * 1e-15
            # cps == cross-power spectrum of im0 and im2

802
            temp = np.array(fft_arr0 * fft_arr1.conjugate()) / (np.abs(fft_arr0) * np.abs(fft_arr1) + eps)
803
804
805

            time0 = time.time()
            if 'pyfft' in globals():
806
                ifft_arr = pyfftw.FFTW(temp,np.empty_like(temp), axes=(0,1), direction='FFTW_BACKWARD')()
807
808
809
810
811
812
813
814
815
816
817
818
819
            else:
                ifft_arr = np.fft.ifft2(temp)
            if self.v: print('backward FFTW: %.2fs' %(time.time() -time0))

            cps = np.abs(ifft_arr)
            # scps = shifted cps
            scps = np.fft.fftshift(cps)
            if self.v:
                PLT.subplot_imshow([in_arr0.astype(np.uint16), in_arr1.astype(np.uint16), fft_arr0.astype(np.uint8),
                                fft_arr1.astype(np.uint8), scps], titles=['matching window im0', 'matching window im1',
                                "fft result im0", "fft result im1", "cross power spectrum"], grid=True)
                PLT.subplot_3dsurface(scps.astype(np.float32))
        else:
820
            self.success = False
821
822
823
824
825
826
827
828
829
830
831
832
833
            self.tracked_errors.append(
                RuntimeError('The matching window became too small for calculating a reliable match. Matching failed.'))
            if self.ignErr:
                scps = None
            else:
                raise self.tracked_errors[-1]

        self.fftw_win_size_YX = wsYX
        return scps


    @staticmethod
    def _get_peakpos(scps):
834
835
836
837
838
        """Returns the row/column position of the peak within the given cross power spectrum.

        :param scps: <np.ndarray> shifted cross power spectrum
        :return:     <np.ndarray> [row, column>
        """
839
        max_flat_idx = np.argmax(scps)
840
        return np.array(np.unravel_index(max_flat_idx, scps.shape))
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882


    @staticmethod
    def _get_shifts_from_peakpos(peakpos, arr_shape):
        y_shift = peakpos[0]-arr_shape[0]//2
        x_shift = peakpos[1]-arr_shape[1]//2
        return x_shift,y_shift


    @staticmethod
    def _clip_image(im, center_YX, winSzYX): # TODO this is also implemented in GeoArray
        get_bounds = lambda YX,wsY,wsX: (int(YX[1]-(wsX/2)),int(YX[1]+(wsX/2)),int(YX[0]-(wsY/2)),int(YX[0]+(wsY/2)))
        wsY,wsX    = winSzYX
        xmin,xmax,ymin,ymax = get_bounds(center_YX,wsY,wsX)
        return im[ymin:ymax,xmin:xmax]


    def _get_grossly_deshifted_images(self, im0, im1, x_intshift, y_intshift): # TODO this is also implemented in GeoArray # this should update ref.win.data and shift.win.data
        # get_grossly_deshifted_im0
        old_center_YX = np.array(im0.shape)/2
        new_center_YX = [old_center_YX[0]+y_intshift, old_center_YX[1]+x_intshift]

        x_left  = new_center_YX[1]
        x_right = im0.shape[1]-new_center_YX[1]
        y_above = new_center_YX[0]
        y_below = im0.shape[0]-new_center_YX[0]
        maxposs_winsz = 2*min(x_left,x_right,y_above,y_below)

        gdsh_im0 = self._clip_image(im0, new_center_YX, [maxposs_winsz, maxposs_winsz])

        # get_corresponding_im1_clip
        crsp_im1  = self._clip_image(im1, np.array(im1.shape) / 2, gdsh_im0.shape)

        if self.v:
            PLT.subplot_imshow([self._clip_image(im0, old_center_YX, gdsh_im0.shape), crsp_im1],
                               titles=['reference original', 'target'], grid=True)
            PLT.subplot_imshow([gdsh_im0, crsp_im1], titles=['reference virtually shifted', 'target'], grid=True)
        return gdsh_im0,crsp_im1


    @staticmethod
    def _find_side_maximum(scps, v=0):
883
        centerpos     = [scps.shape[0]//2, scps.shape[1]//2]
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
        profile_left  = scps[ centerpos [0]  ,:centerpos[1]+1]
        profile_right = scps[ centerpos [0]  , centerpos[1]:]
        profile_above = scps[:centerpos [0]+1, centerpos[1]]
        profile_below = scps[ centerpos [0]: , centerpos[1]]

        if v:
            max_count_vals = 10
            PLT.subplot_2dline([[range(len(profile_left)) [-max_count_vals:], profile_left[-max_count_vals:]],
                                [range(len(profile_right))[:max_count_vals] , profile_right[:max_count_vals]],
                                [range(len(profile_above))[-max_count_vals:], profile_above[-max_count_vals:]],
                                [range(len(profile_below))[:max_count_vals:], profile_below[:max_count_vals]]],
                                titles =['Profile left', 'Profile right', 'Profile above', 'Profile below'],
                                shapetuple=(2,2))

        get_sidemaxVal_from_profile = lambda pf: np.array(pf)[::-1][1] if pf[0]<pf[-1] else np.array(pf)[1]
        sm_dicts_lr  = [{'side':si, 'value': get_sidemaxVal_from_profile(pf)} \
                        for pf,si in zip([profile_left,profile_right],['left','right'])]
        sm_dicts_ab  = [{'side':si, 'value': get_sidemaxVal_from_profile(pf)} \
                        for pf,si in zip([profile_above,profile_below],['above','below'])]
        sm_maxVal_lr = max([i['value'] for i in sm_dicts_lr])
        sm_maxVal_ab = max([i['value'] for i in sm_dicts_ab])
        sidemax_lr   = [sm for sm in sm_dicts_lr if sm['value'] is sm_maxVal_lr][0]
        sidemax_ab   = [sm for sm in sm_dicts_ab if sm['value'] is sm_maxVal_ab][0]
        sidemax_lr['direction_factor'] = {'left':-1, 'right':1} [sidemax_lr['side']]
        sidemax_ab['direction_factor'] = {'above':-1,'below':1} [sidemax_ab['side']]

        if v:
            print('Horizontal side maximum found %s. value: %s' %(sidemax_lr['side'],sidemax_lr['value']))
912
            print('Vertical side maximum found %s. value: %s'   %(sidemax_ab['side'],sidemax_ab['value']))
913
914
915
916
917
918
919
920
921
922

        return sidemax_lr, sidemax_ab


    def _calc_integer_shifts(self, scps):
        peakpos = self._get_peakpos(scps)
        x_intshift, y_intshift = self._get_shifts_from_peakpos(peakpos, scps.shape)
        return x_intshift, y_intshift


923
    def _calc_shift_reliability(self, scps):
924
925
926
927
928
929
930
931
932
933
934
935
936
937
        """Calculates a confidence percentage that can be used as an assessment for reliability of the calculated shifts.

        :param scps:    <np.ndarray> shifted cross power spectrum
        :return:
        """

        # calculate mean power at peak
        peakR, peakC  = self._get_peakpos(scps)
        power_at_peak = np.mean(scps[peakR-1:peakR+2, peakC-1:peakC+2])

        # calculate mean power without peak + 3* standard deviation
        scps_masked        = scps
        scps_masked[peakR-1:peakR+2, peakC-1:peakC+2] = -9999
        scps_masked        = np.ma.masked_equal(scps_masked, -9999)
938
        power_without_peak = np.mean(scps_masked) + 2* np.std(scps_masked)
939
940
941
942
943
944

        # calculate confidence
        confid = 100-((power_without_peak/power_at_peak)*100)
        confid = 100 if confid > 100 else 0 if confid < 0 else confid

        if not self.q:
945
            print('Estimated reliability of the calculated shifts:  %.1f' %confid, '%')
946
947
948
949

        return confid


950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
    def _validate_integer_shifts(self, im0, im1, x_intshift, y_intshift):

        if (x_intshift, y_intshift)!=(0,0):
            # temporalily deshift images on the basis of calculated integer shifts
            gdsh_im0, crsp_im1 = self._get_grossly_deshifted_images(im0, im1, x_intshift, y_intshift)

            # check if integer shifts are now gone (0/0)
            scps = self._calc_shifted_cross_power_spectrum(gdsh_im0, crsp_im1)
            if scps is not None:
                peakpos = self._get_peakpos(scps)
                x_shift, y_shift = self._get_shifts_from_peakpos(peakpos, scps.shape)
                if (x_shift, y_shift) == (0,0):
                    return 'valid', 0, 0, scps
                else:
                    return 'invalid', x_shift, y_shift, scps
            else:
                return 'invalid', None, None, scps
        else:
            return 'valid', 0, 0, None


971
    def _calc_subpixel_shifts(self, scps):
972
973
974
975
976
977
978
979
980
981
982
        sidemax_lr, sidemax_ab = self._find_side_maximum(scps, self.v)
        x_subshift = (sidemax_lr['direction_factor']*sidemax_lr['value'])/(np.max(scps)+sidemax_lr['value'])
        y_subshift = (sidemax_ab['direction_factor']*sidemax_ab['value'])/(np.max(scps)+sidemax_ab['value'])
        return x_subshift, y_subshift


    @staticmethod
    def _get_total_shifts(x_intshift, y_intshift, x_subshift, y_subshift):
        return x_intshift+x_subshift, y_intshift+y_subshift


983
984
985
986
987
988
989
990
991
992
993
994
995
996
    def _get_deshifted_otherWin(self):
        """Returns a de-shifted version of self.otherWin as a GeoArray instance.The output dimensions and geographic
        bounds are equal to those of self.matchWin and geometric shifts are corrected according to the previously
        computed X/Y shifts within the matching window. This allows direct application of algorithms e.g. measuring
        image similarity.

        The image subset that is resampled in this function is always the same that has been resampled during
        computation of geometric shifts (usually the image with the higher geometric resolution).

        :returns:   GeoArray instance of de-shifted self.otherWin
        """

        # shift vectors have been calculated to fit target image onto reference image
        # -> so the shift vectors have to be inverted if shifts are applied to reference image
997
998
        coreg_info = self._get_inverted_coreg_info() if self.otherWin.imID=='ref' else self.coreg_info

999
1000
        matchFull  = self.ref if self.matchWin.imID=='ref' else self.shift
        otherFull  = self.ref if self.otherWin.imID=='ref' else self.shift