CoReg_local.py 33.4 KB
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# -*- coding: utf-8 -*-

import warnings
import os
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from copy import copy
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# custom
try:
    import gdal
except ImportError:
    from osgeo import gdal
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try:
    import pyfftw
except ImportError:
    pyfftw = None
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import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

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from .Tie_Point_Grid import Tie_Point_Grid
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from .CoReg import COREG
from .DeShifter import DESHIFTER
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from py_tools_ds.geo.coord_trafo import transform_any_prj, reproject_shapelyGeometry
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from py_tools_ds.geo.map_info import geotransform2mapinfo
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from geoarray import GeoArray
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__author__ = 'Daniel Scheffler'
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class COREG_LOCAL(object):
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    """See help(COREG_LOCAL) for documentation!"""

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    def __init__(self, im_ref, im_tgt, grid_res, max_points=None, window_size=(256, 256), path_out=None, fmt_out='ENVI',
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                 out_crea_options=None, projectDir=None, r_b4match=1, s_b4match=1, max_iter=5, max_shift=5,
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                 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):
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        """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
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        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.
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        :param im_ref(str, GeoArray):   source path of reference image (any GDAL compatible image format is supported)
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        :param im_tgt(str, GeoArray):   source path of image to be shifted (any GDAL compatible image format is
                                        supported)
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        :param grid_res:                quality grid resolution in pixels of the target image (x-direction)
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        :param max_points(int):         maximum number of points used to find coregistration tie points
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                                        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.
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        :param window_size(tuple):      custom matching window size [pixels] (default: (256,256))
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        :param path_out(str):           target path of the coregistered image
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                                            - if None (default), no output is written to disk
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                                            - if 'auto': /dir/of/im1/<im1>__shifted_to__<im0>.bsq
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        :param fmt_out(str):            raster file format for output file. ignored if path_out is None. Can be any GDAL
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                                        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.
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        :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.
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        :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)
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        :param tieP_filter_level(int):  filter tie points used for shift correction in different levels (default: 3).
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                                        NOTE: lower levels are also included if a higher level is chosen
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                                            - Level 0: no tie point filtering
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                                            - 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
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                                                correction does not increase image similarity within matching window
                                                (measured by mean structural similarity index)
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                                            - Level 3: RANSAC outlier detection
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        :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)
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                                        HINT: decrease this value in case of poor signal-to-noise ratio of your input
                                              data
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        :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%)
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        :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)
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                                        valid algorithms: nearest, bilinear, cubic, cubic_spline, lanczos, average,
                                                          mode, max, min, med, q1, q3
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                                        default: cubic
        :param resamp_alg_calc(str)     the resampling algorithm to be used for all warping processes during calculation
                                        of spatial shifts
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                                        (valid algorithms: nearest, bilinear, cubic, cubic_spline, lanczos, average,
                                                           mode, max, min, med, q1, q3)
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                                        default: cubic (highly recommended)
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        :param footprint_poly_ref(str): footprint polygon of the reference image (WKT string or
                                        shapely.geometry.Polygon),
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                                        e.g. 'POLYGON ((299999 6000000, 299999 5890200, 409799 5890200, 409799 6000000,
                                                        299999 6000000))'
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        :param footprint_poly_tgt(str): footprint polygon of the image to be shifted (WKT string or
                                        shapely.geometry.Polygon)
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                                        e.g. 'POLYGON ((299999 6000000, 299999 5890200, 409799 5890200, 409799 6000000,
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                                                        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.
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        :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
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                                        (default: True; deactivated if 'data_corners_im0' and 'data_corners_im1' are
                                        given)
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        :param binary_ws(bool):         use binary X/Y dimensions for the matching window (default: True)
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        :param force_quadratic_win(bool):   force a quadratic matching window (default: 1)
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        :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.
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        :param CPUs(int):               number of CPUs to use during calculation of geometric quality grid
                                        (default: None, which means 'all CPUs available')
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        :param progress(bool):          show progress bars (default: True)
        :param v(bool):                 verbose mode (default: False)
        :param q(bool):                 quiet mode (default: False)
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        :param ignore_errors(bool):     Useful for batch processing. (default: False)
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        """
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        # assertions
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        assert gdal.GetDriverByName(fmt_out), "'%s' is not a supported GDAL driver." % fmt_out
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        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.'

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

        self.imref = GeoArray(im_ref, nodata=nodata[0], progress=progress, q=q)
        self.im2shift = GeoArray(im_tgt, nodata=nodata[1], progress=progress, q=q)
        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
        self.max_points = max_points
        self.window_size = window_size
        self.max_shift = max_shift
        self.max_iter = max_iter
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        self.tieP_filter_level = tieP_filter_level
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        self.min_reliability = min_reliability
        self.rs_max_outlier = rs_max_outlier
        self.rs_tolerance = rs_tolerance
        self.align_grids = align_grids
        self.match_gsd = match_gsd
        self.out_gsd = out_gsd
        self.target_xyGrid = target_xyGrid
        self.rspAlg_DS = resamp_alg_deshift  # TODO convert integers to strings
        self.rspAlg_calc = resamp_alg_calc
        self.calc_corners = calc_corners
        self.nodata = nodata
        self.outFillVal = outFillVal
        self.bin_ws = binary_ws
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        self.force_quadratic_win = force_quadratic_win
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        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
        self.ignErr = ignore_errors  # FIXME this is not yet implemented for COREG_LOCAL
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        assert self.tieP_filter_level in range(4), 'Invalid tie point filter level.'
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        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 ' \
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            'instances do not seem to belong to the GeoArray class. If you are working in Jupyter Notebook, reset ' \
            'the kernel and try again.'
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        COREG.__dict__['_set_outpathes'](self, self.imref, self.im2shift)
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        # make sure that the output directory of coregistered image is the project directory if a project directory is
        # given
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        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()
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        try:
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            # ignore_errors must be False because in case COREG init fails, coregistration for the whole scene fails
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            self.COREG_obj = COREG(self.imref, self.im2shift,
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                                   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,
                                   mask_baddata_ref=None,  # see below
                                   mask_baddata_tgt=None,
                                   CPUs=self.CPUs,
                                   force_quadratic_win=self.force_quadratic_win,
                                   binary_ws=self.bin_ws,
                                   progress=self.progress,
                                   v=v,
                                   q=q,
                                   ignore_errors=False)
        except Exception:
            warnings.warn('\nFirst attempt to check if functionality of co-registration failed. Check your '
                          'input data and parameters. The following error occurred:', stacklevel=3)
            raise
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        if pyfftw:
            self.check_if_fftw_works()
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        # add bad data mask
        # (mask is not added during initialization of COREG object in order to avoid bad data area errors there)
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        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
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        self._tiepoint_grid = None  # set by self.quality_grid
        self._CoRegPoints_table = None  # set by self.CoRegPoints_table
        self._coreg_info = None  # set by self.coreg_info
        self.deshift_results = None  # set by self.correct_shifts()
        self._success = None  # set by self.success property
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    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
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        try:
            self.COREG_obj.q = True
            self.COREG_obj.v = False
            self.COREG_obj.calculate_spatial_shifts()
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        except RuntimeError:
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            if self.COREG_obj.fftw_works is not None:
                pass
            else:
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                warnings.warn('\nFirst attempt to check if functionality of co-registration failed. Check your '
                              'input data and parameters. The following error occurred:', stacklevel=3)
                raise
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        self.COREG_obj.q = self.q
        self.COREG_obj.v = self.v

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    @property
    def projectDir(self):
        if self._projectDir:
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            if len(os.path.split(self._projectDir)) == 1:
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                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
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            root_dir = os.path.dirname(self.im2shift.filePath) if self.im2shift.filePath else os.path.curdir
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            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)
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            return self._projectDir

    @property
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    def tiepoint_grid(self):
        if self._tiepoint_grid:
            return self._tiepoint_grid
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        else:
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            self._tiepoint_grid = Tie_Point_Grid(self.COREG_obj, self.grid_res,
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                                                 max_points=self.max_points,
                                                 outFillVal=self.outFillVal,
                                                 resamp_alg_calc=self.rspAlg_calc,
                                                 tieP_filter_level=self.tieP_filter_level,
                                                 outlDetect_settings=dict(
                                                     min_reliability=self.min_reliability,
                                                     rs_max_outlier=self.rs_max_outlier,
                                                     rs_tolerance=self.rs_tolerance),
                                                 dir_out=self.projectDir,
                                                 CPUs=self.CPUs,
                                                 progress=self.progress,
                                                 v=self.v,
                                                 q=self.q)
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            self._tiepoint_grid.get_CoRegPoints_table()

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            if self.v:
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                print('Visualizing CoReg points grid...')
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                self.view_CoRegPoints(figsize=(10, 10))
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            return self._tiepoint_grid
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    @property
    def CoRegPoints_table(self):
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        """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.
        """
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        return self.tiepoint_grid.CoRegPoints_table
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    @property
    def success(self):
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        self._success = self.tiepoint_grid.GCPList != []
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        if not self._success and not self.q:
            warnings.warn('No valid GCPs could by identified.')
        return self._success

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    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()

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    def view_CoRegPoints(self, attribute2plot='ABS_SHIFT', cmap=None, exclude_fillVals=True, backgroundIm='tgt',
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                         hide_filtered=True, figsize=None, savefigPath='', savefigDPI=96, showFig=True,
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                         vmin=None, vmax=None, return_map=False, zoomable=False):
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        """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')
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        :param exclude_fillVals:    <bool> whether to exclude those points of the grid where spatial shift detection
                                    failed
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        :param backgroundIm:        <str> whether to use the target or the reference image as map background. Possible
                                          options are 'ref' and 'tgt' (default: 'tgt')
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        :param hide_filtered:       <bool> hide all points that have been filtered out according to tie point filter
                                    level
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        :param figsize:             <tuple> size of the figure to be viewed, e.g. (10,10)
        :param savefigPath:
        :param savefigDPI:
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        :param showFig:             <bool> whether to show or to hide the figure
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        :param vmin:
        :param vmax:
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        :param return_map           <bool>
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        :param zoomable:            <bool> enable or disable zooming via mpld3
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        :return:
        """

        # get a map showing target image
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        if backgroundIm not in ['tgt', 'ref']:
            raise ValueError('backgroundIm')
        backgroundIm = self.im2shift if backgroundIm == 'tgt' else self.imref
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        fig, ax, map2show = backgroundIm.show_map(figsize=figsize, nodataVal=self.nodata[1], return_map=True,
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                                                  band=self.COREG_obj.shift.band4match, zoomable=zoomable)
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        plt.tick_params(axis='both', which='major', labelsize=40)
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        # ax.tick_params(axis='both', which='minor', labelsize=8)
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        # 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
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        outlierCols = [c for c in self.CoRegPoints_table.columns if 'OUTLIER' in c]
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        attr2include = ['geometry', attribute2plot] + outlierCols + ['X_SHIFT_M', 'Y_SHIFT_M']
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        GDF = self.CoRegPoints_table.loc\
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            [self.CoRegPoints_table.X_SHIFT_M != self.outFillVal, attr2include].copy() \
            if exclude_fillVals else self.CoRegPoints_table.loc[:, attr2include]
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        # get LonLat coordinates for all points
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        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]))))
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        # get colors for all points
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        # vmin = min(GDF[GDF[attribute2plot] != self.outFillVal][attribute2plot])
        # vmax = max(GDF[GDF[attribute2plot] != self.outFillVal][attribute2plot])
        # norm = mpl_normalize(vmin=vmin, vmax=vmax)
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        palette = cmap if cmap is not None else plt.cm.RdYlGn_r
        if cmap is None and attribute2plot == 'ANGLE':
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            # import matplotlib.colors as mcolors
            # colors1 = plt.cm.RdYlGn_r(np.linspace(0., 1, 128))
            # colors2 = plt.cm.RdYlGn(np.linspace(0., 1, 128))
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            # combine them and build a new colormap
            # colors  = np.vstack((colors1, colors2))
            # palette = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors)
            # palette = plt.cm.hsv
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            import cmocean
            palette = cmocean.cm.delta
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        # GDF['color'] = [*GDF[attribute2plot].map(lambda val: palette(norm(val)))]
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        # add quality grid to map
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        # 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)
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        GDF['plt_XY'] = list(GDF['LonLat'].map(lambda ll: map2show(*ll)))
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        GDF['plt_X'] = list(GDF['plt_XY'].map(lambda XY: XY[0]))
        GDF['plt_Y'] = list(GDF['plt_XY'].map(lambda XY: XY[1]))
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        if hide_filtered:
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            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()
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        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()
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                plt.scatter(GDF_filt['plt_X'], GDF_filt['plt_Y'], c='b', marker=marker, s=250, alpha=1.0,
                            label='reliability')
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            if self.tieP_filter_level > 1:
                # flag level 2 outliers
                GDF_filt = GDF[GDF.L2_OUTLIER == True].copy()
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                plt.scatter(GDF_filt['plt_X'], GDF_filt['plt_Y'], c='r', marker=marker, s=150, alpha=1.0, label='MSSIM')
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            if self.tieP_filter_level > 2:
                # flag level 3 outliers
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                GDF_filt = GDF[GDF.L3_OUTLIER == True].copy()
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                plt.scatter(GDF_filt['plt_X'], GDF_filt['plt_Y'], c='y', marker=marker, s=250, alpha=1.0,
                            label='RANSAC')
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            if self.tieP_filter_level > 0:
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                plt.legend(loc=0, scatterpoints=1)
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        # plot all points on top
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        if not GDF.empty:
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            vmin_auto, vmax_auto = (np.percentile(GDF[attribute2plot], 0), np.percentile(GDF[attribute2plot], 95)) \
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                if attribute2plot != 'ANGLE' else (0, 360)
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            vmin = vmin if vmin is not None else vmin_auto
            vmax = vmax if vmax is not None else vmax_auto

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

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

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

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        if return_map:
            return fig, ax, map2show

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        if showFig and not self.q:
            plt.show(block=True)
        else:
            plt.close(fig)

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    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:
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            import folium
            import geojson
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            from folium import plugins
        except ImportError:
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            folium, geojson, plugins = [None] * 3
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        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
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        center_lon, center_lat = (lon_min + lon_max) / 2, (lat_min + lat_max) / 2
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        # get image to plot
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        image2plot = self.im2shift[:, :, 0]  # FIXME hardcoded band
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        from py_tools_ds.geo.raster.reproject import warp_ndarray
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        image2plot, gt, prj = \
            warp_ndarray(image2plot, self.im2shift.geotransform, self.im2shift.projection,
                         in_nodata=self.nodata[1], out_nodata=self.nodata[1], out_XYdims=(1000, 1000), q=True,
                         out_prj='epsg:3857')  # image must be transformed into web mercator projection
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        # create map
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        map_osm = folium.Map(location=[center_lat, center_lon])  # ,zoom_start=3)
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        import matplotlib
        plugins.ImageOverlay(
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            colormap=lambda x: (1, 0, 0, x),  # TODO a colormap must be given
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            # colormap=matplotlib.cm.gray, # does not work
            image=image2plot, bounds=[[lat_min, lon_min], [lat_max, lon_max]],
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        ).add_to(map_osm)
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        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)
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        gjs = geojson.Feature(geometry=overlapPoly, properties={})
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        folium.GeoJson(gjs).add_to(map_osm)

        return map_osm

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    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."""

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        original_map_info = geotransform2mapinfo(self.im2shift.gt, self.im2shift.prj)
        updated_map_info = copy(original_map_info)
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        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

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

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        if self._coreg_info:
            return self._coreg_info
        else:
            self._coreg_info = {
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                'GCPList': self.tiepoint_grid.GCPList,
                '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},
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                'updated map info means': self._get_updated_map_info_meanShifts(),
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                'original map info': geotransform2mapinfo(self.imref.gt, self.imref.prj),
                'reference projection': self.imref.prj,
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                'reference geotransform': self.imref.gt,
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                '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
                'success': self.success
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            }
            return self.coreg_info

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    def correct_shifts(self, max_GCP_count=None, cliptoextent=False, min_points_local_corr=5):
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        """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
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        :param cliptoextent:  <bool> whether to clip the output image to its real extent
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        :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)
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        :return:
        """
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        coreg_info = self.coreg_info
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        if self.tiepoint_grid.GCPList:
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            if max_GCP_count:
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                coreg_info['GCPList'] = coreg_info['GCPList'][:max_GCP_count]
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            DS = DESHIFTER(self.im2shift, coreg_info,
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                           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)
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            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!')