CoReg_local.py 33.8 KB
Newer Older
1
2
3
4
5
# -*- coding: utf-8 -*-
__author__='Daniel Scheffler'

import warnings
import os
6
from copy import copy
7
8
9
10
11
12

# custom
try:
    import gdal
except ImportError:
    from osgeo import gdal
13
14
15
16
try:
    import pyfftw
except ImportError:
    pyfftw = None
17
18
19
20
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

21
from .Tie_Point_Grid import Tie_Point_Grid
22
23
from .CoReg import COREG
from .DeShifter import DESHIFTER
24
from py_tools_ds.geo.coord_trafo import transform_any_prj, reproject_shapelyGeometry
25
from py_tools_ds.geo.map_info import geotransform2mapinfo
26
from geoarray import GeoArray
27
28
29
30



class COREG_LOCAL(object):
31
32
    """See help(COREG_LOCAL) for documentation!"""

33
    def __init__(self, im_ref, im_tgt, grid_res, max_points=None, window_size=(256,256), path_out=None, fmt_out='ENVI',
34
                 out_crea_options=None, projectDir=None, r_b4match=1, s_b4match=1, max_iter=5, max_shift=5,
35
36
37
38
39
40
                 tieP_filter_level=3, min_reliability=60, rs_max_outlier=10, rs_tolerance=2.5, align_grids=True,
                 match_gsd=False, 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,
                 outFillVal=-9999, nodata=(None, None), calc_corners=True, binary_ws=True, force_quadratic_win=True,
                 mask_baddata_ref=None, mask_baddata_tgt=None, CPUs=None, progress=True, v=False, q=False,
                 ignore_errors=True):
41
42
43

        """Applies the algorithm to detect spatial shifts to the whole overlap area of the input images. Spatial shifts
        are calculated for each point in grid of which the parameters can be adjusted using keyword arguments. Shift
44
45
        correction performs a polynomial transformation using the calculated shifts of each point in the grid as GCPs.
        Thus this class can be used to correct for locally varying geometric distortions of the target image.
46
47
48

        :param im_ref(str, GeoArray):   source path of reference image (any GDAL compatible image format is supported)
        :param im_tgt(str, GeoArray):   source path of image to be shifted (any GDAL compatible image format is supported)
49
        :param grid_res:                quality grid resolution in pixels of the target image (x-direction)
50
        :param max_points(int):         maximum number of points used to find coregistration tie points
51
52
53
                                        NOTE: Points are selected randomly from the given point grid (specified by
                                        'grid_res'). If the point does not provide enough points, all available points
                                        are chosen.
54
        :param window_size(tuple):      custom matching window size [pixels] (default: (256,256))
55
        :param path_out(str):           target path of the coregistered image
56
                                            - if None (default), no output is written to disk
57
                                            - if 'auto': /dir/of/im1/<im1>__shifted_to__<im0>.bsq
58
        :param fmt_out(str):            raster file format for output file. ignored if path_out is None. Can be any GDAL
59
60
                                        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.
61
62
63
64
        :param out_crea_options(list):  GDAL creation options for the output image,
                                        e.g. ["QUALITY=80", "REVERSIBLE=YES", "WRITE_METADATA=YES"]
        :param projectDir(str):         name of a project directory where to store all the output results. If given,
                                        name is inserted into all automatically generated output paths.
65
66
67
68
        :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 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)
Daniel Scheffler's avatar
Daniel Scheffler committed
69
        :param tieP_filter_level(int):  filter tie points used for shift correction in different levels (default: 3).
70
                                        NOTE: lower levels are also included if a higher level is chosen
71
                                            - Level 0: no tie point filtering
72
73
74
                                            - Level 1: Reliablity filtering - filter all tie points out that have a low
                                                reliability according to internal tests
                                            - Level 2: SSIM filtering - filters all tie points out where shift
75
76
                                                correction does not increase image similarity within matching window
                                                (measured by mean structural similarity index)
77
                                            - Level 3: RANSAC outlier detection
78
79
80
81
82
83
84
        :param min_reliability(float):  Tie point filtering: minimum reliability threshold, below which tie points are
                                        marked as false-positives (default: 60%)
                                        - accepts values between 0% (no reliability) and 100 % (perfect reliability)
                                        HINT: decrease this value in case of poor signal-to-noise ratio of your input data
        :param rs_max_outlier(float):   RANSAC tie point filtering: proportion of expected outliers (default: 10%)
        :param rs_tolerance(float):     RANSAC tie point filtering: percentage tolerance for max_outlier_percentage
                                                (default: 2.5%)
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
        :param out_gsd (float):         output pixel size in units of the reference coordinate system (default = pixel
                                        size of the input array), given values are overridden by match_gsd=True
        :param align_grids (bool):      True: align the input coordinate grid to the reference (does not affect the
                                        output pixel size as long as input and output pixel sizes are compatible
                                        (5:30 or 10:30 but not 4:30), default = True
        :param match_gsd (bool):        True: match the input pixel size to the reference pixel size,
                                        default = False
        :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'.
        :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)
103
104
105
106
107
        :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,
108
109
110
111
112
                                                        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.
113
114
115
116
117
        :param outFillVal(int):         if given the generated geometric quality grid is filled with this value in case
                                        no match could be found during co-registration (default: -9999)
        :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
118
                                        (default: True; deactivated if 'data_corners_im0' and 'data_corners_im1' are given
119
        :param binary_ws(bool):         use binary X/Y dimensions for the matching window (default: True)
Daniel Scheffler's avatar
Daniel Scheffler committed
120
        :param force_quadratic_win(bool):   force a quadratic matching window (default: 1)
121
122
123
124
125
126
127
128
129
130
131
132
133
134
        :param mask_baddata_ref(str, BadDataMask):
                                        path to a 2D boolean mask file (or an instance of BadDataMask) 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, BadDataMask):
                                        path to a 2D boolean mask file (or an instance of BadDataMask) 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.
135
136
        :param CPUs(int):               number of CPUs to use during calculation of geometric quality grid
                                        (default: None, which means 'all CPUs available')
137
138
139
        :param progress(bool):          show progress bars (default: True)
        :param v(bool):                 verbose mode (default: False)
        :param q(bool):                 quiet mode (default: False)
140
        :param ignore_errors(bool):     Useful for batch processing. (default: False)
141
        """
142

143
        # assertions
144
        assert gdal.GetDriverByName(fmt_out), "'%s' is not a supported GDAL driver." % fmt_out
145
146
        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.'
147

148
        self.params            = dict([x for x in locals().items() if x[0] != "self" and not x[0].startswith('__')])
149

150
151
        self.imref             = GeoArray(im_ref, nodata=nodata[0], progress=progress, q=q)
        self.im2shift          = GeoArray(im_tgt, nodata=nodata[1], progress=progress, q=q)
152
153
154
155
156
        self.path_out          = path_out  # updated by self.set_outpathes
        self.fmt_out           = fmt_out
        self.out_creaOpt       = out_crea_options
        self._projectDir       = projectDir
        self.grid_res          = grid_res
157
        self.max_points        = max_points
158
159
160
        self.window_size       = window_size
        self.max_shift         = max_shift
        self.max_iter          = max_iter
161
        self.tieP_filter_level = tieP_filter_level
162
163
164
        self.min_reliability   = min_reliability
        self.rs_max_outlier    = rs_max_outlier
        self.rs_tolerance      = rs_tolerance
165
166
167
168
        self.align_grids       = align_grids
        self.match_gsd         = match_gsd
        self.out_gsd           = out_gsd
        self.target_xyGrid     = target_xyGrid
Daniel Scheffler's avatar
Daniel Scheffler committed
169
        self.rspAlg_DS         = resamp_alg_deshift # TODO convert integers to strings
170
        self.rspAlg_calc       = resamp_alg_calc
171
172
173
174
        self.calc_corners      = calc_corners
        self.nodata            = nodata
        self.outFillVal        = outFillVal
        self.bin_ws            = binary_ws
Daniel Scheffler's avatar
Daniel Scheffler committed
175
        self.force_quadratic_win = force_quadratic_win
176
177
178
179
180
        self.CPUs              = CPUs
        self.path_verbose_out  = '' # TODO
        self.v                 = v
        self.q                 = q if not v else False        # overridden by v
        self.progress          = progress if not q else False # overridden by v
181
        self.ignErr            = ignore_errors # FIXME this is not yet implemented for COREG_LOCAL
182

183
        assert self.tieP_filter_level in range(4), 'Invalid tie point filter level.'
184
185
        assert isinstance(self.imref, GeoArray) and isinstance(self.im2shift, GeoArray), \
            'Something went wrong with the creation of GeoArray instances for reference or target image. The created ' \
186
            'instances do not seem to belong to the GeoArray class. If you are working in Jupyter Notebook, reset the '\
187
188
            'kernel and try again.'

189
190
191
192
193
194
        COREG.__dict__['_set_outpathes'](self, self.imref, self.im2shift)
        # make sure that the output directory of coregistered image is the project directory if a project directory is given
        if path_out and projectDir and os.path.basename(self.path_out):
            self.path_out = os.path.join(self.projectDir, os.path.basename(self.path_out))

        gdal.AllRegister()
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209

        try:
            self.COREG_obj = COREG(self.imref, self.im2shift,
                                   ws                 = window_size,
                                   footprint_poly_ref = footprint_poly_ref,
                                   footprint_poly_tgt = footprint_poly_tgt,
                                   data_corners_ref   = data_corners_ref,
                                   data_corners_tgt   = data_corners_tgt,
                                   resamp_alg_calc    = self.rspAlg_calc,
                                   calc_corners       = calc_corners,
                                   r_b4match          = r_b4match,
                                   s_b4match          = s_b4match,
                                   max_iter           = max_iter,
                                   max_shift          = max_shift,
                                   nodata             = nodata,
Daniel Scheffler's avatar
Daniel Scheffler committed
210
211
                                   mask_baddata_ref   = None, # see below
                                   mask_baddata_tgt   = None,
212
                                   CPUs               = self.CPUs,
Daniel Scheffler's avatar
Daniel Scheffler committed
213
                                   force_quadratic_win = self.force_quadratic_win,
214
215
216
217
                                   binary_ws          = self.bin_ws,
                                   progress           = self.progress,
                                   v                  = v,
                                   q                  = q,
218
                                   ignore_errors      = False) # must be False because COREG init fails, coregistration for the whole scene fails
219
220
        except Exception as err:
            raise RuntimeError('First attempt to check if functionality of co-registration failed. Check your '
221
                               'input data and parameters. The following error occurred: \n%s' %repr(err))
222

223
224
        if pyfftw:
            self.check_if_fftw_works()
225

226

227
228
        # add bad data mask
        # (mask is not added during initialization of COREG object in order to avoid bad data area errors there)
229
230
        if mask_baddata_ref is not None: self.COREG_obj.ref  .mask_baddata = mask_baddata_ref
        if mask_baddata_tgt is not None: self.COREG_obj.shift.mask_baddata = mask_baddata_tgt
231

Daniel Scheffler's avatar
Daniel Scheffler committed
232
        self._tiepoint_grid      = None # set by self.quality_grid
233
        self._CoRegPoints_table = None # set by self.CoRegPoints_table
234
        self._coreg_info        = None # set by self.coreg_info
235
        self.deshift_results    = None # set by self.correct_shifts()
236
        self._success           = None # set by self.success property
237
238


239
240
241
242
    def check_if_fftw_works(self):
        """Assigns the attribute 'fftw_works' to self.COREG_obj by executing shift calculation once with muted output.
        """
        # calculate global shift once in order to check is fftw works
243
244
245
246
        try:
            self.COREG_obj.q = True
            self.COREG_obj.v = False
            self.COREG_obj.calculate_spatial_shifts()
247
        except RuntimeError as err:
248
249
250
            if self.COREG_obj.fftw_works is not None:
                pass
            else:
251
252
                raise RuntimeError('First attempt to check if functionality of co-registration failed. Check your '
                                   'input data and parameters. The following error occurred: ', repr(err))
253

Daniel Scheffler's avatar
Daniel Scheffler committed
254
255
256
        self.COREG_obj.q = self.q
        self.COREG_obj.v = self.v

257

258
259
260
261
262
263
264
265
266
    @property
    def projectDir(self):
        if self._projectDir:
            if len(os.path.split(self._projectDir))==1:
                return os.path.abspath(os.path.join(os.path.curdir, self._projectDir))
            else:
                return os.path.abspath(self._projectDir)
        else:
            # return a project name that not already has a corresponding folder on disk
267
268
269
270
271
272
273
            root_dir  = os.path.dirname(self.im2shift.filePath) if self.im2shift.filePath else os.path.curdir
            fold_name = 'UntitledProject_1'

            while os.path.isdir(os.path.join(root_dir, fold_name)):
                fold_name = '%s_%s' % (fold_name.split('_')[0], int(fold_name.split('_')[-1]) + 1)

            self._projectDir = os.path.join(root_dir, fold_name)
274
275
276
277
            return self._projectDir


    @property
Daniel Scheffler's avatar
Daniel Scheffler committed
278
279
280
    def tiepoint_grid(self):
        if self._tiepoint_grid:
            return self._tiepoint_grid
281
        else:
Daniel Scheffler's avatar
Daniel Scheffler committed
282
283
284
285
286
            self._tiepoint_grid = Tie_Point_Grid(self.COREG_obj, self.grid_res,
                                                 max_points        = self.max_points,
                                                 outFillVal        = self.outFillVal,
                                                 resamp_alg_calc   = self.rspAlg_calc,
                                                 tieP_filter_level = self.tieP_filter_level,
287
288
289
290
                                                 outlDetect_settings = dict(
                                                     min_reliability = self.min_reliability,
                                                     rs_max_outlier  = self.rs_max_outlier,
                                                     rs_tolerance    = self.rs_tolerance),
Daniel Scheffler's avatar
Daniel Scheffler committed
291
292
293
294
295
                                                 dir_out           = self.projectDir,
                                                 CPUs              = self.CPUs,
                                                 progress          = self.progress,
                                                 v                 = self.v,
                                                 q                 = self.q)
296
297
            self._tiepoint_grid.get_CoRegPoints_table()

298
            if self.v:
Daniel Scheffler's avatar
Daniel Scheffler committed
299
                print('Visualizing CoReg points grid...')
300
                self.view_CoRegPoints(figsize=(10,10))
Daniel Scheffler's avatar
Daniel Scheffler committed
301
            return self._tiepoint_grid
302
303
304
305


    @property
    def CoRegPoints_table(self):
306
307
308
309
        """Returns a GeoDataFrame with the columns 'geometry','POINT_ID','X_IM','Y_IM','X_UTM','Y_UTM','X_WIN_SIZE',
        'Y_WIN_SIZE','X_SHIFT_PX','Y_SHIFT_PX', 'X_SHIFT_M', 'Y_SHIFT_M', 'ABS_SHIFT' and 'ANGLE' containing all
        information containing all the results frm coregistration for all points in the geometric quality grid.
        """
Daniel Scheffler's avatar
Daniel Scheffler committed
310

Daniel Scheffler's avatar
Daniel Scheffler committed
311
        return self.tiepoint_grid.CoRegPoints_table
312
313


314
315
    @property
    def success(self):
Daniel Scheffler's avatar
Daniel Scheffler committed
316
        self._success = self.tiepoint_grid.GCPList != []
317
318
319
320
321
        if not self._success and not self.q:
            warnings.warn('No valid GCPs could by identified.')
        return self._success


322
323
324
325
326
327
    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."""
        return self.COREG_obj.show_image_footprints()


328
    def view_CoRegPoints(self, attribute2plot='ABS_SHIFT', cmap=None, exclude_fillVals=True, backgroundIm='tgt',
329
                         hide_filtered=True, figsize=None, savefigPath='', savefigDPI=96, showFig=True,
330
                         vmin=None, vmax=None, return_map=False, zoomable=False):
331
332
333
334
335
336
337
338
        """Shows a map of the calculated quality grid with the target image as background.

        :param attribute2plot:      <str> the attribute of the quality grid to be shown (default: 'ABS_SHIFT')
        :param cmap:                <plt.cm.<colormap>> a custom color map to be applied to the plotted grid points
                                                        (default: 'RdYlGn_r')
        :param exclude_fillVals:    <bool> whether to exclude those points of the grid where spatial shift detection failed
        :param backgroundIm:        <str> whether to use the target or the reference image as map background. Possible
                                          options are 'ref' and 'tgt' (default: 'tgt')
339
        :param hide_filtered:       <bool> hide all points that have been filtered out according to tie point filter level
340
341
342
        :param figsize:             <tuple> size of the figure to be viewed, e.g. (10,10)
        :param savefigPath:
        :param savefigDPI:
343
        :param showFig:             <bool> whether to show or to hide the figure
344
345
        :param vmin:
        :param vmax:
346
        :param return_map           <bool>
347
        :param zoomable:            <bool> enable or disable zooming via mpld3
348
349
350
351
352
353
        :return:
        """

        # get a map showing target image
        if backgroundIm not in ['tgt','ref']: raise ValueError('backgroundIm')
        backgroundIm      = self.im2shift if backgroundIm=='tgt' else self.imref
354
        fig, ax, map2show = backgroundIm.show_map(figsize=figsize, nodataVal=self.nodata[1], return_map=True,
355
                                                  band=self.COREG_obj.shift.band4match, zoomable=zoomable)
356
357
358
359

        plt.tick_params(axis='both', which='major', labelsize=40)
        #ax.tick_params(axis='both', which='minor', labelsize=8)

360
361
362
363
        # fig, ax, map2show = backgroundIm.show_map_utm(figsize=(20,20), nodataVal=self.nodata[1], return_map=True)
        plt.title(attribute2plot)

        # transform all points of quality grid to LonLat
364
        outlierCols  = [c for c in self.CoRegPoints_table.columns if 'OUTLIER' in c]
365
        attr2include = ['geometry', attribute2plot] + outlierCols + ['X_SHIFT_M', 'Y_SHIFT_M']
366
        GDF = self.CoRegPoints_table.loc\
367
368
                [self.CoRegPoints_table.X_SHIFT_M != self.outFillVal, attr2include].copy() \
                if exclude_fillVals else self.CoRegPoints_table.loc[:, attr2include]
369
370
371
372
373
374
375
376
377

        # get LonLat coordinates for all points
        get_LonLat    = lambda X, Y: transform_any_prj(self.im2shift.projection, 4326, X, Y)
        GDF['LonLat'] = list(GDF['geometry'].map(lambda geom: get_LonLat(*tuple(np.array(geom.coords.xy)[:,0]))))

        # get colors for all points
        #vmin = min(GDF[GDF[attribute2plot] != self.outFillVal][attribute2plot])
        #vmax = max(GDF[GDF[attribute2plot] != self.outFillVal][attribute2plot])
        #norm = mpl_normalize(vmin=vmin, vmax=vmax)
378
379
380
381
382
383
384
385
386
387
388
389
390
        palette = cmap if cmap is not None else plt.cm.RdYlGn_r
        if cmap is None and attribute2plot == 'ANGLE':
            #import matplotlib.colors as mcolors
            #colors1 = plt.cm.RdYlGn_r(np.linspace(0., 1, 128))
            #colors2 = plt.cm.RdYlGn(np.linspace(0., 1, 128))

            ## combine them and build a new colormap
            #colors  = np.vstack((colors1, colors2))
            #palette = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors)
            #palette = plt.cm.hsv

            import cmocean
            palette = cmocean.cm.delta
391
392
393
394
395
396
397
398
        #GDF['color'] = [*GDF[attribute2plot].map(lambda val: palette(norm(val)))]

        # add quality grid to map
        #plot_point = lambda row: ax.plot(*map2show(*row['LonLat']), marker='o', markersize=7.0, alpha=1.0, color=row['color'])
        #GDF.apply(plot_point, axis=1)
        GDF['plt_XY'] = list(GDF['LonLat'].map(lambda ll: map2show(*ll)))
        GDF['plt_X']  = list(GDF['plt_XY'].map(lambda XY: XY[0]))
        GDF['plt_Y']  = list(GDF['plt_XY'].map(lambda XY: XY[1]))
399

400
        if hide_filtered:
401
402
403
            if self.tieP_filter_level > 0:  GDF = GDF[GDF.L1_OUTLIER == False].copy()
            if self.tieP_filter_level > 1:  GDF = GDF[GDF.L2_OUTLIER == False].copy()
            if self.tieP_filter_level > 2:  GDF = GDF[GDF.L3_OUTLIER == False].copy()
404
405
406
407
408
        else:
            marker = 'o' if len(GDF) < 10000 else '.'
            if self.tieP_filter_level > 0:
                # flag level 1 outliers
                GDF_filt = GDF[GDF.L1_OUTLIER == True].copy()
409
                plt.scatter(GDF_filt['plt_X'], GDF_filt['plt_Y'], c='b', marker=marker, s=250, alpha=1.0, label='reliability')
410
411
412
            if self.tieP_filter_level > 1:
                # flag level 2 outliers
                GDF_filt = GDF[GDF.L2_OUTLIER == True].copy()
413
                plt.scatter(GDF_filt['plt_X'], GDF_filt['plt_Y'], c='r', marker=marker, s=150, alpha=1.0, label='MSSIM')
414
415
            if self.tieP_filter_level > 2:
                # flag level 3 outliers
416
                GDF_filt = GDF[GDF.L3_OUTLIER == True].copy()
417
418
419
420
                plt.scatter(GDF_filt['plt_X'], GDF_filt['plt_Y'], c='y', marker=marker, s=250, alpha=1.0, label='RANSAC')

            if self.tieP_filter_level > 0:
                plt.legend(loc=0, scatterpoints = 1)
421
422


423
        # plot all points on top
Daniel Scheffler's avatar
Daniel Scheffler committed
424
        if not GDF.empty:
425
426
427
428
429
            vmin_auto, vmax_auto = (np.percentile(GDF[attribute2plot], 0), np.percentile(GDF[attribute2plot], 95)) \
                                    if attribute2plot!='ANGLE' else (0, 360)
            vmin = vmin if vmin is not None else vmin_auto
            vmax = vmax if vmax is not None else vmax_auto

430
            points = plt.scatter(GDF['plt_X'],GDF['plt_Y'], c=GDF[attribute2plot], lw = 0,
Daniel Scheffler's avatar
Daniel Scheffler committed
431
432
433
                                 cmap=palette, marker='o' if len(GDF)<10000 else '.', s=50, alpha=1.0,
                                 vmin=vmin, vmax=vmax)

434
435
436
            # plot shift vectors
            #map2show.quiver(GDF['plt_X'], GDF['plt_Y'], GDF['X_SHIFT_M'], GDF['Y_SHIFT_M'])#, scale=700)

Daniel Scheffler's avatar
Daniel Scheffler committed
437
438
439
440
441
442
443
444
445
            # add colorbar
            divider = make_axes_locatable(plt.gca())
            cax = divider.append_axes("right", size="2%",
                                      pad=0.1)  # create axis on the right; size =2% of ax; padding = 0.1 inch
            plt.colorbar(points, cax=cax)
        else:
            if not self.q:
                warnings.warn('Cannot plot any tie point because none is left after tie point validation.')

446
447
448
449

        if savefigPath:
            fig.savefig(savefigPath, dpi=savefigDPI)

450
451
452
        if return_map:
            return fig, ax, map2show

453
454
455
456
457
        if showFig and not self.q:
            plt.show(block=True)
        else:
            plt.close(fig)

458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476

    def view_CoRegPoints_folium(self, attribute2plot='ABS_SHIFT', cmap=None, exclude_fillVals=True):
        warnings.warn(UserWarning('This function is still under construction and may not work as expected!'))
        assert self.CoRegPoints_table is not None, 'Calculate quality grid first!'

        try:
            import folium, geojson
            from folium import plugins
        except ImportError:
            folium, geojson, plugins = [None]*3
        if not folium or not geojson:
            raise ImportError("This method requires the libraries 'folium' and 'geojson'. They can be installed with "
                              "the shell command 'pip install folium geojson'.")

        lon_min, lat_min, lon_max, lat_max = \
            reproject_shapelyGeometry(self.im2shift.box.mapPoly, self.im2shift.projection, 4326).bounds
        center_lon, center_lat = (lon_min+lon_max)/2, (lat_min+lat_max)/2

        # get image to plot
477
        image2plot = self.im2shift[:,:,0] # FIXME hardcoded band
478

479
        from py_tools_ds.geo.raster.reproject import warp_ndarray
480
481
        image2plot, gt, prj = warp_ndarray(image2plot, self.im2shift.geotransform, self.im2shift.projection,
                                           in_nodata=self.nodata[1], out_nodata=self.nodata[1],
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
                                           out_XYdims=(1000, 1000), q=True, out_prj='epsg:3857') # image must be transformed into web mercator projection

        # create map
        map_osm = folium.Map(location=[center_lat, center_lon])#,zoom_start=3)
        import matplotlib
        plugins.ImageOverlay(
            colormap=lambda x: (1, 0, 0, x), # TODO a colormap must be given
            # colormap=matplotlib.cm.gray, # does not work
            image=image2plot, bounds=[[lat_min, lon_min], [lat_max, lon_max]],
            ).add_to(map_osm)

        folium.GeoJson(self.CoRegPoints_table.loc[:, ['geometry', attribute2plot]]).add_to(map_osm)

        # add overlap polygon
        overlapPoly = reproject_shapelyGeometry(self.COREG_obj.overlap_poly, self.im2shift.epsg, 4326)
        gjs         = geojson.Feature(geometry=overlapPoly, properties={})
        folium.GeoJson(gjs).add_to(map_osm)


        return map_osm


504
505
506
507
508
509
510
511
512
513
    def _get_updated_map_info_meanShifts(self):
        """Returns the updated map info of the target image, shifted on the basis of the mean X/Y shifts."""

        original_map_info   = geotransform2mapinfo(self.im2shift.gt, self.im2shift.prj)
        updated_map_info    = copy(original_map_info)
        updated_map_info[3] = str(float(original_map_info[3]) + self.tiepoint_grid.mean_x_shift_map)
        updated_map_info[4] = str(float(original_map_info[4]) + self.tiepoint_grid.mean_y_shift_map)
        return updated_map_info


514
515
    @property
    def coreg_info(self):
516
517
518
        """A dictionary containing all the information needed to correct the detected local displacements of the target
        image."""

519
520
521
522
        if self._coreg_info:
            return self._coreg_info
        else:
            self._coreg_info = {
Daniel Scheffler's avatar
Daniel Scheffler committed
523
                'GCPList'               : self.tiepoint_grid.GCPList,
524
525
526
527
528
529
                'mean_shifts_px'        : {'x':self.tiepoint_grid.mean_x_shift_px,
                                           'y':self.tiepoint_grid.mean_y_shift_px},
                'mean_shifts_map'       : {'x':self.tiepoint_grid.mean_x_shift_map,
                                           'y':self.tiepoint_grid.mean_y_shift_map},
                'updated map info means': self._get_updated_map_info_meanShifts(),
                'original map info'     : geotransform2mapinfo(self.imref.gt, self.imref.prj),
530
                'reference projection'  : self.imref.prj,
Daniel Scheffler's avatar
Daniel Scheffler committed
531
                'reference geotransform': self.imref.gt,
532
533
534
                'reference grid'        : [ [self.imref.gt[0], self.imref.gt[0]+self.imref.gt[1]],
                                            [self.imref.gt[3], self.imref.gt[3]+self.imref.gt[5]] ],
                'reference extent'      : {'cols':self.imref.xgsd, 'rows':self.imref.ygsd}, # FIXME not needed anymore
535
                'success'               : self.success
536
537
538
            }
            return self.coreg_info

539

540
    def correct_shifts(self, max_GCP_count=None, cliptoextent=False, min_points_local_corr=5):
541
542
543
544
        """Performs a local shift correction using all points from the previously calculated geometric quality grid
        that contain valid matches as GCP points.

        :param max_GCP_count: <int> maximum number of GCPs to use
545
        :param cliptoextent:  <bool> whether to clip the output image to its real extent
546
547
548
        :param min_points_local_corr:   <int> number of valid tie points, below which a global shift correction is performed
                                        instead of a local correction (global X/Y shift is then computed as the mean shift
                                        of the remaining points)(default: 5 tie points)
549
550
        :return:
        """
551
552

        coreg_info = self.coreg_info
553

Daniel Scheffler's avatar
Daniel Scheffler committed
554
        if self.tiepoint_grid.GCPList:
555
            if max_GCP_count:
556
                coreg_info['GCPList'] = coreg_info['GCPList'][:max_GCP_count]
557
558

            DS = DESHIFTER(self.im2shift, coreg_info,
559
560
561
562
563
564
565
566
567
568
569
570
571
572
                           path_out              = self.path_out,
                           fmt_out               = self.fmt_out,
                           out_crea_options      = self.out_creaOpt,
                           align_grids           = self.align_grids,
                           match_gsd             = self.match_gsd,
                           out_gsd               = self.out_gsd,
                           target_xyGrid         = self.target_xyGrid,
                           min_points_local_corr = min_points_local_corr,
                           resamp_alg            = self.rspAlg_DS,
                           cliptoextent          = cliptoextent,
                           #clipextent            = self.im2shift.box.boxMapYX,
                           progress              = self.progress,
                           v                     = self.v,
                           q                     = self.q)
573
574
575
576
577
578

            self.deshift_results = DS.correct_shifts()
            return self.deshift_results
        else:
            if not self.q:
                warnings.warn('Correction of geometric shifts failed because the input GCP list is empty!')